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Artificial Intelligence in Oncology: Transforming Cancer Carepptx

Artificial Intelligence (AI) is transforming oncology by enabling faster, more accurate, and personalized cancer care. AI uses technologies like machine learning (ML), deep learning (DL), and natural language processing (NLP) to analyze complex medical data, helping doctors make better decisions at every stage of cancer care. Key Applications of AI in Oncology: Cancer Detection & Diagnosis: AI analyzes radiology (CT, MRI, mammography) and pathology images to detect tumors early and accurately. Tools like PathAI, Aidoc, and DeepMind are used in clinical imaging. Precision Medicine & Genomics: AI interprets genetic mutations to match patients with targeted therapies. Platforms like Tempus and OncoKB support personalized cancer treatment. Radiation Oncology: AI automates contouring, treatment planning, and dose optimization. Reduces planning time and improves consistency in radiation delivery. Drug Discovery & Clinical Trials: AI helps discover new cancer drugs and match patients to trials. Companies like BenevolentAI and Insilico Medicine lead this space. Prognostics & Monitoring: AI predicts survival, recurrence, and side effects using patient data. Wearables and mobile apps track symptoms and alert doctors in real time. Benefits of AI in Oncology: Early and accurate diagnosis Faster workflows and reduced workload Personalized treatments Improved patient outcomes Challenges: Data privacy and bias Lack of clinical validation Interpretability of AI decisions Integration into hospital systems Conclusion: AI is not replacing oncologists but empowering them with better tools to diagnose, plan, and treat cancer more effectively. With ongoing research and responsible use, AI will play a crucial role in the future of oncology.

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AI x Oncology: Power of Intelligence ⚖️Black Box & Ethics 🧬 Digital Twins 🎨 Generative AI 🔍 AI & Biopsy 🧠 Multimodal AI ️ 🗂️Real-World Evidence ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Smart Tumor Boards Dr. Sumit Kumar Assistant Professor NEIGRIHMS, Shillong, Meghalaya IBM Watson for Oncology PathAI DeepMind Health (Google Health) Zebra Medical Vision Arterys Aidoc CancerIQ Tempus
• Rising Cancer Cases: Over 20 million new cases globally expected by 2025 — too much for manual systems alone. • Data Explosion: Imaging, genomics, pathology, EHRs — more data than any human can process alone. • Need for Precision: One-size-fits-all treatment is outdated — AI helps personalize care. • Time Pressure on Oncologists: Burnout is real — AI can assist, not replace. • Early Detection & Better Outcomes: AI can spot subtle signs before humans do. • Speed + Scale: AI can analyze thousands of records/images in minutes. Why AI in Oncology? "In 2030, will you be working with AI — or watching it work instead of you?"
What is AI? 🤖 AI 📈 ML 🧠 DL Machines that think like humans Learn from data & improve over time Learns from large data using brain-like layers Can follow rules, make decisions Finds patterns (e.g., tumor risk) Great for images, pathology, scans Example: Chatbot, tumor board support Example: Predicting recurrence Example: Detecting cancer in CT/biopsy slides 🧠 = Think like a human 📊 = Learn from data 🧬 = Think deeply using layers "AI is the brain, ML is how it learns, and DL is how it learns really deep stuff — like cancer scans." Simple Real-Life Examples: •AI: A chatbot giving cancer information •ML: Predicting if a lung nodule is benign or malignant based on past data •DL: Recognizing cancer in pathology slides or CT scans automatically
Scope of AI in Oncology 🔍 Diagnosis ️ 🖼️Imaging 💊 Treatment Planning Detects cancer early Auto-detects tumors in CT, MRI, PET Suggests personalized protocols Analyzes biopsy/pathology Segments organs/tumors Assists in radiation/chemo plans Identifies rare subtypes Reduces human error Adaptive RT planning 📈 Prognosis 🔬 Research Predicts survival/outcome Speeds drug discovery Identifies high-risk patients Simulates clinical trials Tracks recurrence risk Analyzes big datasets 🧭 Where AI Is Making an Impact “AI is now involved in almost every step of cancer care — from spotting it early to finding better treatments faster.”
AI in Medical Imaging 🚀 AI Capabilities 🩻 Clinical Applications Early tumor detection CT, MRI, PET, mammography Automated organ & tumor contouring Radiotherapy planning (e.g., VMAT, IMRT) Flags suspicious lesions Lung nodules, brain mets, liver lesions Accelerates scan interpretation Faster reporting, reduced workload Enhances diagnostic accuracy Minimizes errors, improves consistency “AI in imaging acts as a second pair of eyes — faster, sharper, and tireless.”
🔬 Real-World Examples in Oncology Tool / Project Use Case Google DeepMind Breast cancer detection from mammograms Aidoc Emergency triage: flags hemorrhages, embolisms Zebra Medical Vision Detects osteoporosis, fatty liver, lung nodules Qure.ai (India-based) TB and lung cancer screening via chest X-ray Arterys MRI and CT analysis for oncology and cardiology “AI doesn’t replace radiologists — it’s like giving them super-vision. It sees faster, deeper, and often catches what we might miss.” ⚙️AI Techniques Used: •Convolutional Neural Networks (CNNs): Best for 2D/3D imaging •Radiomics: Converts images into data for AI to analyze patterns •Natural Language Processing (NLP): Reads radiology reports for trends
AI in Pathology – How It Works 🧠 AI Role ⚙️How AI Does It Digital slide analysis Whole-slide images (WSI) are scanned and fed into deep learning models trained to detect cancerous regions. Pattern recognition AI uses Convolutional Neural Networks (CNNs) to spot abnormal cellular architecture, color variations, and tissue structures. Quantitative measurements AI counts cells, calculates nuclear size, mitotic activity, and other metrics faster and more objectively than humans. Predictive modeling Combines pathology images with genetic or clinical data to predict response to treatment, risk of recurrence, or survival. Workflow optimization Triage algorithms rank slides by urgency, alerting pathologists to high-risk or abnormal cases first. "Pathology is going digital — and AI is making slide reading faster, more consistent, and even predictive."
Real-Life Examples of AI in Pathology ️ 🛠️Tool / Company 📍 Use Case Paige.AI Detects prostate and breast cancer in whole-slide images with high accuracy (FDA approved) PathAI AI-powered pathology for cancer diagnosis and prognosis; used in clinical trials and research Ibex Medical Analytics AI-based detection of breast, prostate, and gastric cancers; deployed in hospitals globally Google Health Developed AI to assist in grading prostate cancer and detecting metastatic breast cancer Aiforia Cloud-based platform for AI-assisted analysis in pathology labs and education Owkin Combines pathology with genomics and outcomes to predict therapy response in trials “These tools aren’t just experimental — some are FDA-cleared and already helping pathologists in real hospitals.”
AI in Treatment Planning 💡 AI Power 🔍 What It Does 🎯 Why It Matters 🧬 Personalized Planning Matches treatment to tumor biology & patient data Improves outcomes, avoids one- size-fits-all 🎯 Radiation Smart Plans Auto-contours, predicts dose impact Saves time, increases precision 🔄 Adaptive Therapy Adjusts plan mid-treatment Responds to real-time patient changes ⚠️Toxicity Prediction Forecasts side effects early Prevents harm, supports shared decision-making 🧠 Smart Decision Tools Recommends evidence-based regimens Supports clinician with data- backed insights "Think of AI as your co-planner — one that learns from millions of cases to help design the most effective plan for your patient."
️ 🛠️Real-World AI Tools in Treatment Planning 🔧 Tool 🧠 Function 🌍 Used In RayStation AI-driven radiation planning & optimization Advanced radiotherapy centers Limbus AI Auto-contouring for radiation therapy North America, Europe CureMatch Personalized drug combination suggestions Precision oncology clinics IBM Watson for Oncology Evidence-based treatment recommendations Global hospitals & research Varian Ethos Adaptive radiation therapy Online adaptive RT clinics “These tools aren’t science fiction — they’re transforming real clinics, cutting planning time, and elevating care quality.”
AI in Prognosis & Predictive Analytics 🧠 AI Role 📊 What It Does 🌍 Clinical Impact Survival Prediction Uses clinical, genetic, and imaging data to predict patient survival Tailors follow-up schedules, guides treatment choices Recurrence Risk Identifies high-risk patients based on tumor features and patient history Focuses treatment on high-risk groups Treatment Response Predicts how patients will respond to therapies based on data from past cases Optimizes therapy regimens Early Detection of Complications Forecasts potential side effects or complications from treatment Prevents adverse events, improves quality of life Genomic Risk Stratification Combines genetic data to determine likelihood of relapse or metastasis Enables personalized, more effective treatments "AI predicts the future — helping doctors make proactive treatment decisions." Google Health’s AI predicts breast cancer recurrence with 95% accuracy.
AI in Drug Discovery 🧠 AI Role ⚙️How It Works Target Identification Analyzes genomic & proteomic data to find cancer-driving molecules Molecule Generation Uses generative AI to design drug-like molecules (e.g., via GANs, RL) Virtual Screening Rapidly tests 1000s of compounds via computer simulation (no lab needed) Efficacy & Toxicity Prediction Trains on past trial data to forecast if a drug will work or harm Trial Optimization Matches the right patients to the right trials using EMR + AI models 🧪 Real-World Examples: •Atomwise – Uses AI to discover cancer-fighting molecules •BenevolentAI – Identifies novel drug targets in oncology •Insilico Medicine – Designed a preclinical cancer drug in < 45 days •Exscientia – AI-designed anti-cancer drugs already in trials “What used to take years, AI now does in months — it's changing the speed and precision of drug discovery.”
AI in Clinical Trials 🧠 AI Function 🔍 What It Does Patient Matching Identifies eligible patients using EMRs & genomics Trial Design Optimization Simulates outcomes to improve trial structure Site Selection Finds best trial locations based on patient access Monitoring & Safety Tracks data in real time, flags adverse events Predictive Analytics Forecasts trial success or failure early Real-World Examples: •Deep 6 AI – Finds matching patients for oncology trials in minutes •Unlearn.AI – Builds digital twins to reduce control group size •Tempus – Uses AI to optimize precision oncology trial enrollment •TriNetX – Real-world data platform for faster recruitment "AI doesn’t just speed up clinical trials — it makes them smarter, safer, and more inclusive."
AI in Oncology – Limitations 🚫 Limitation 🧠 Issue Data Bias Skewed results due to unbalanced datasets Black Box Models Lack of transparency in AI decision-making Generalizability Poor performance outside training data Over-reliance on AI May reduce clinician judgment Data Quality Issues Garbage in, garbage out problem "Even the best tech can fail without careful design, oversight, and ethical guardrails."
Real-World Examples: AI Limitations in Oncology 📌 IBM Watson for Oncology Trained mostly on U.S. data, it provided inappropriate treatment suggestions in non-U.S. settings due to data bias. 📌 AI for Lung Cancer Detection Deep learning model performed well but couldn't explain why, leading to trust issues among radiologists. 📌 U.S. vs. Europe CT Scan Models AI trained on U.S. data struggled with European scans — a classic case of poor generalizability. 📌 AI Overuse in Skin Cancer Detection In a UK hospital, doctors began over-relying on AI outputs, ignoring clinical judgment — led to missed diagnoses.
🧩 AI in Oncology – Ethical Concerns ⚖️Concern 🔍 Why It Matters Patient Privacy Risk of data breaches or misuse Informed Consent Patients may not know AI is being used Accountability Unclear who is responsible for AI errors Equity & Access AI tools may not benefit all populations Regulatory Gaps Lack of clear laws and approval pathways “AI is a powerful tool — but not a perfect one. We need transparency, ethics, and constant human oversight.”
🟠 Real-World Examples: Ethical Concerns in AI Use 📌 DeepMind & NHS Data Scandal (UK) AI used for kidney disease detection accessed patient data without full consent — raised privacy and transparency concerns. 📌 ER Triage Tools (U.S.) Some AI tools used to prioritize emergency cases didn't inform patients, breaching informed consent norms. 📌 Liability in Drug Dosing Errors AI recommended an incorrect dose; unclear if fault lay with software, developer, or clinician — a gray zone of accountability. 📌 AI Accessibility Gap in Rural Oncology Centers High-end AI tools were unavailable in low-resource settings, worsening healthcare disparity.
AI in Radiation Oncology 🤖 AI Application 💡 What It Does Auto-Contouring Automatically segments tumors & organs on CT/MRI Dose Optimization Suggests optimal radiation plans for better OAR sparing Adaptive Radiotherapy Adjusts plans in real-time based on anatomy changes Quality Assurance (QA) Detects planning or delivery errors faster Image Registration Aligns multimodal images (CT, MRI, PET) with precision 🧪 Real-World Tools: •Mirada, MIM, Limbus AI – Auto-contouring for faster planning •RayStation, Ethos (Varian) – AI-powered adaptive planning •MVision AI – Cloud-based OAR contouring •Google DeepMind + UCL – AI segmentation for head & neck radiotherapy "AI reduces hours of manual work into minutes — freeing up time for more patient-focused care."
AI in Medical Oncology AI Application What It Does Treatment Decision Support AI helps oncologists choose the most effective treatment based on patient data. Genomic Profiling AI analyzes tumor DNA to identify mutations that drive cancer. Risk Prediction AI predicts cancer recurrence or metastasis based on patient history and biomarkers. Personalized Treatment AI designs custom treatment plans based on genetic and clinical data. Clinical Trial Matching AI matches patients with appropriate clinical trials based on their genomic and clinical profile. 🌍 Real-World Tools: •IBM Watson for Oncology – Personalized treatment recommendations. •Tempus – Genomic and clinical data-driven insights for precision oncology. •Foundation Medicine – Provides molecular profiling to guide personalized cancer therapy. •Guardant Health – Liquid biopsy with AI to detect actionable mutations. "AI in medical oncology enables precise and individualized treatment, ensuring patients receive the right care at the right time."
AI in Surgical Oncology –🔍 Before & During Surgery AI Application What It Does Real-World Tool 3D Surgical Planning Converts imaging into 3D models for precise tumor mapping. Surgimap (Brainlab) Real-Time Navigation Guides instruments during surgery using AI + imaging. Medtronic StealthStation Tumor Detection Identifies tumor vs healthy tissue intraoperatively. PathAI with surgical microscopes Robotic Precision AI-assisted robotic arms improve accuracy & reduce errors. da Vinci Surgical System “AI is like a surgical co-pilot — mapping, guiding, and watching for trouble, all in real time.”
AI in Surgical Oncology –After Surgery & Risk Management AI Application What It Does Real-World Tool Complication Prediction Identifies risk of bleeding, infection, or poor healing. MySurgeryRisk (Johns Hopkins) Recovery Monitoring Tracks vitals and recovery trends using AI algorithms. Vitals AI dashboards in smart ICUs Surgical Outcome Analysis Reviews surgery videos/data to improve future planning. Proximie AI-assisted video review Clinical Decision Support Recommends next steps post- surgery based on outcomes. IBM Watson Health (legacy platform) “AI doesn’t just end in the OR — it follows the patient, predicts risks, and improves recovery.”
Clinical Decision Support Systems (CDSS) in Oncology Function What It Does Diagnosis Assistance Suggests likely cancer types based on imaging, pathology, and clinical data. Treatment Recommendations Provides evidence-based options tailored to tumor type, stage, and biomarkers. Risk Stratification Predicts outcomes, recurrence risk, or toxicity based on patient- specific data. Clinical Trial Matching Matches patients to ongoing trials using molecular and clinical profiles. Alerts & Reminders Notifies clinicians about missing tests, drug interactions, or new guidelines.
️ 🛠️Real-World Tools & Platforms IBM Watson for Oncology – Offers evidence-based treatment recommendations (used in select centers). OncoKB + cBioPortal – Integrates genomics with treatment options. NAVIFY Tumor Board (Roche) – Multidisciplinary decision support with AI integration. Tempus – Combines clinical and molecular data to guide treatment. “CDSS doesn’t replace clinical judgment — it enhances it by turning complex data into actionable insights.” Clinical Decision Support Systems (CDSS) in Oncology
AI Tools for Risk Stratification Tool What It Predicts Cancer Type CanRisk Hereditary cancer risk (BRCA, etc.) Breast, Ovarian MySurgeryRisk Post-op complications Surgical oncology OncoNPC Cancer origin (unknown primary) CUP (Cancer Unknown Primary) REMBRANDT AI Survival risk based on molecular data Brain tumors (GBM) Tempus xT Recurrence & treatment response Lung, Colon, Breast 🎤 “AI helps us stratify patients — who needs more care, and when.”
Real-Time Monitoring & Adaptive Therapy AI Application What It Does Benefit Wearable Monitoring Tracks vitals, symptoms, activity in real time Early detection of complications Imaging-Based Adaptation Updates treatment plans based on tumor response Personalized RT/CT regimens Remote Patient Monitoring (RPM) Flags issues during treatment (e.g., toxicity) Prevents hospital readmissions Dose Adaptation in RT Adjusts radiotherapy dose as tumor shrinks Improves accuracy, reduces harm ️ 🛠️Real-World Tools •CureMetrix AI – Monitors imaging changes during therapy. •Varian Ethos – Real-time adaptive radiotherapy. •Biofourmis – AI for continuous home monitoring. •OncoHealth – Tracks patient symptoms during treatment. “With AI, monitoring doesn’t stop at the hospital door — it adjusts care as the patient lives it.”
NLP in Oncology Records: Extracting Data from EHRs NLP Application What It Does Benefit Clinical Data Extraction Extracts structured data from unstructured EHR notes Saves time, improves data accuracy Disease Identification Identifies cancer types, stages, and history from text Faster diagnosis & better tracking Treatment History Analyzes previous treatments, side effects, and outcomes Personalized care decisions Clinical Trial Matching Matches patients with clinical trials using EHR data Faster recruitment, targeted trials 🛠️Real-World Tools •TruCode Encoder – Converts clinical narratives into structured data •DeepMind NLP – AI for extracting clinical insights from EHRs •ClinicalBERT – NLP model for medical text classification •Oncology Data Hub – NLP for oncology-specific data extraction 🎤 “NLP transforms unstructured EHR text into powerful, actionable insights for better patient care.”
AI in Palliative Care Planning Personalized Care • Symptom Management • Prognostication AI Role What It Does Impact Symptom Prediction Identifies and predicts symptoms (e.g., pain, fatigue) Improves proactive symptom management Personalized Care Plans Customizes care based on patient data and preferences Enhances quality of life Prognostication Predicts disease progression and survival outcomes Helps with end-of-life planning Care Coordination Analyzes data to optimize team communication Streamlines care delivery 🛠️Real-World Tools •Pathfinder (Aidoc) – AI for identifying end-of-life care needs •AliveCor AI – Tracks patient vitals for palliative care adjustments •TruHealth AI – Uses AI to predict symptom exacerbations in palliative patients 🎤 “AI in palliative care helps predict needs, customize plans, and improve comfort during a difficult time.”
Predictive Models for Survival & Recurrence AI Function What It Does Impact Survival Prediction Estimates patient life expectancy based on clinical & genomic data Informs treatment goals & planning Recurrence Risk Predicts chances of cancer coming back Guides follow-up & surveillance Treatment Outcome Modeling Forecasts response to therapies Supports personalized decisions Risk Stratification Classifies patients into low/high-risk groups Enables targeted care 🛠️Real-World AI Tools •Adjuvant! Online – Survival predictions for early-stage breast cancer •PREDICT (UK) – Breast cancer survival and benefit from therapy •Tempus AI – Recurrence predictions using multi-omics data •IBM Watson for Oncology – Predicts outcomes and treatment efficacy 🎤 “AI doesn’t just react to cancer — it helps us stay one step ahead.”
AI in Tumor Board Decision-Making Smarter, Faster, Unified Clinical Decisions AI Role What It Does Impact Case Summarization Extracts key data from EHRs, scans, labs Saves time, improves clarity Treatment Recommendation Suggests evidence-based options Informed, guideline-driven choices Outcome Prediction Projects survival, recurrence, side effects Personalized planning Decision Support Tools Standardizes decisions across institutions Reduces variation in care 🛠️Real-World Tools •IBM Watson for Oncology – Clinical decision support •Navify Tumor Board (Roche) – Integrates patient data for board meetings •OncoLens – AI-powered case review and workflow optimization •CureMatch – Matches genomic profiles to personalized treatment options 🎤 “AI gives tumor boards speed, structure, and sharper decisions — all backed by data.”
🎯 Case Example: Recurrent Esophageal Cancer Patient scenario: • Male, 60 years, squamous cell carcinoma of the mid-esophagus • Treated with NACTRT 1 year ago • Now presents with recurrence at the primary site and new liver lesion Simplified Impact of AI • You suspect recurrence → AI highlights lesion growth and flags recurrence • You plan FOLFIRI or immunotherapy → AI shows historical response rates from global databases • You think of trial enrollment → AI finds 2 relevant trials nearby that fit this molecular profile • You want a second check → AI gives recommendation confidence levels based on published evidence 🔍 AI Doesn’t Replace — It Reinforces “I know the protocols — AI backs me up with patterns, rare options, and predictive insights.”
AI in Cancer Follow-up & Compliance AI supports long-term care, not just active treatment Function What It Does Why It Matters Appointment Management Reminds and reschedules follow-up visits and tests Improves continuity, avoids missed care Medication Adherence Tracks chemo/oral therapy compliance via apps and wearables Reduces treatment failure & relapse risk Symptom Monitoring Uses apps to detect fatigue, nausea, pain in real time Enables early toxicity intervention Recurrence Prediction Analyzes patterns to flag patients at higher recurrence risk Guides personalized surveillance intensity Psychosocial Support AI chatbots screen for anxiety, depression, or isolation Supports mental health & quality of life 🛠️Real-World Tools •Noona – Patient-reported outcomes in oncology •Carevive – AI-guided follow-up and toxicity alerts •CancerAid – Personal cancer care assistant •Jvion – Predictive insights on patient drop-offs & risk
🎯 Follow-Up Case: Breast Cancer Survivor Status: Post-NACT Post-BCS Post-RT Now on Hormonal Therapy ➝ ➝ ➝ Phase AI Application Tool Example Benefit Post-surgery/RT monitoring Tracks symptoms: lymphedema, pain, fatigue Noona, Carevive Early detection of complications Hormonal therapy adherence Sends reminders, tracks missed doses Medisafe, Care4Today Improves compliance, reduces recurrence risk Follow-up coordination Manages imaging/test schedules Navya, CancerAid Ensures timely mammograms, labs Recurrence prediction Analyzes patient data for risk trends Jvion, Oncora AI Helps decide intensity of follow-up Mental health support Chatbots monitor emotional well-being Wysa, Ginger AI Supports quality of life, reduces distress 🔍 How AI Supports Follow-Up 🎤 “Follow-up is more than checking labs — AI makes it proactive, personalized, and patient-centered.”
Case Study – IBM Watson for Oncology 🧠 What is it? AI-powered clinical decision support tool for personalized cancer care. 🏥 Used by: • Apollo Hospitals • Manipal Hospitals 🔧 How it Works: • Analyzes patient data + global literature • Recommends evidence-based treatment plans • Suggests clinical trials 📈 Pros: Speeds up decision- ✔ making Evidence-backed ✔ options Helpful in complex ✔ cases ⚠️Cons: Not always nuanced ✖ Expensive for ✖ individuals Needs constant updates ✖ "Watson helps validate my treatment plan, highlights rare protocols, and saves hours of literature review — but I still make the final call."
Case Study – AI Tools for Individual Oncologists Tool Function Access OncoKB / MyCancerGenome Precision oncology info Free online CureMetrix AI for breast mammograms Demo by request Navya.ai (India) AI + expert opinion system Case submission Tempus Tumor genomics + AI insights Via patient reports Qure.ai Imaging AI (CXR, CT) License-based 💬 Use-Case Example (CA Breast): Post-NACT + BCS + RT + Hormone Therapy ➡️ AI tools can: ➡️ • Flag signs of recurrence early (imaging AI) • Track hormonal adherence (apps) • Match to clinical trials (Tempus/Trialjectory) "Even without Watson, I can use free or light tools to stay smart, scan imaging faster, and follow-up better."
The Black Box Problem – Can We Trust What We Can’t See? 🚫 What Is It? • AI gives results (e.g., “Recommend chemo”) ❌ But doesn’t explain how or why This lack of transparency is the ➡️ black box problem ⚠️Why It Matters in Oncology: • Can’t explain decisions to patients or tumor boards • Risk of errors, hidden biases • Hard to trust in rare or complex cancer cases 💬 Analogy: • “It’s like using a GPS that tells you to turn —but won’t show the map.” ✅ What We Need: • AI tools that show how they reached a decision • Better explainability = better trust in cancer care “ ️ 🗣️ In cancer care, we need clarity, not mystery. Interpretability makes AI trustworthy.”
Why Is This a Problem in Oncology? In oncology, you need to justify every decision Situation Why Black Box is Risky Tumor board discussion You can't explain AI’s suggestion to peers Patient counseling Patient may not trust “AI says so” Legal/ethical If a wrong call is made, who’s responsible? Rare case AI may miss nuance, you won’t know why GPS Analogy AI Analogy GPS tells you “Turn left,” but doesn’t show the map or traffic AI tells you “Treat with chemo,” but doesn’t show the logic or evidence You might take a wrong turn You might make a wrong treatment decision ✅ Simple Analogy
Regulatory Landscape – Who Approves AI in Oncology? 🏢 Regulator 🌍 Region 🔍 Role FDA (U.S.) USA Approves AI-based medical devices & clinical decision tools CE Mark Europe Certifies AI tools as safe & effective for clinical use CDSCO India Evaluates AI medical software (in early stages) 📜 Key Global Regulators:
[Research & Development] ↓ [Data Collection & Training] ↓ [Internal Validation] (Does the AI work accurately?) ↓ [External Validation] (Tested on independent patient data) ↓ [Clinical Trials / Real-World Testing] (Does it help doctors & patients?) ↓ 🎯 Notes: •Without regulatory approval, AI should not guide treatment decisions. •FDA also introduced a Software as a Medical Device (SaMD) framework for AI. •Oncology AI tools often fall under Clinical Decision Support (CDS) rules. [Regulatory Submission] → FDA (USA) / CE Mark (EU) / CDSCO (India) ↓ [Regulatory Review] (Check for safety, effectiveness, transparency) ↓ ✅ [Approval & Certification] (AI tool can now be used in hospitals) ↓ 🏥 [Clinical Integration] (Deployed in oncology clinics & decision workflows)
Validation Challenges in Oncology AI ⚠️Challenge 📌 Description Data Diversity Cancer types, stages, and patient populations vary widely Small Sample Sizes Especially in rare cancers — not enough training data Overfitting Risk AI may perform well on test data but fail in real- world Changing Standards Treatment protocols evolve — AI must adapt too Lack of Gold Standard No perfect benchmark for diagnosis or outcome in some cancers 🔍 Why Validation Is Hard: 💡 Why It Matters: “An unvalidated AI tool may look smart — but can make life-altering mistakes in oncology.”
📦 📦 Case Example: IBM Watson for Oncology – Validation Gap 📉 What Happened: IBM Watson was deployed in several hospitals to help oncologists suggest cancer treatments. In some cases, Watson gave unsafe or irrelevant treatment suggestions. Internal reports showed that Watson’s suggestions were based on synthetic data and limited real-world validation.
AI vs Human Intelligence – Collaboration, Not Replacement 🧠 Human Strengths: • Clinical judgment • Empathy & communication • Handling unexpected situations • Understanding patient values 🤖 AI Strengths: •Analyzing large datasets instantly •Spotting subtle patterns in scans or genes •Predictive modeling •Working 24/7 with no fatigue ⚖️The Sweet Spot: Augmented Oncology “Oncologists + AI = Faster, smarter, and more personalized care.” 💬 Analogy: “AI is the GPS, but the doctor is still driving.”
Legal Implications in Misdiagnosis "When AI suggests wrong, who takes the blame?" 👤 Party 📌 Responsibility Doctor Final decision-maker; must not rely blindly on AI Hospital Responsible if unvalidated AI is deployed or training lacks AI Developer Rarely liable; unless faulty design or undisclosed risks 🔍 Who May Be Liable? ❗ Key Legal Principles: •AI = Assist, Not Replace •Human judgment is non-negotiable •Documentation & reasoning matter in court ️ 🛡️Doctor’s Defense: ✅ Used AI as decision support ✅ Followed guidelines ✅ Documented reasoning ⚠️Bottom Line: Legal accountability stays with humans. AI helps, but doesn’t excuse poor judgment.
Digital Twin in Oncology: “A virtual you — tested before treated.” 💡 Function 🧪 Use in Oncology Simulate treatment Predict chemo/RT response Personalize plans Tailor dosing, avoid toxicity Monitor disease Detect relapse early Trial optimization Run virtual drug tests 🤖 What is it? A digital replica of a cancer patient built from clinical data, scans, genomics & treatment history. 🔍 What Can It Do? 🌍 Real Tools: •Siemens Healthineers – RT simulation •Unlearn.AI – Synthetic trial arms “ ️ 🗣️ Digital twins = practice on the clone, perfect on the patient.”
Generative AI: Your Virtual Cancer Case Trainer 🔍 Definition Generative AI = AI that creates new content like images, text, or data — not just analyze it. In oncology, it helps simulate: 🧬 Rare cancer cases 🖼️Imaging scans (CT, MRI, PET) 🔬 Pathology slides (H&E, IHC) 📋 Treatment plans & tumor board decisions 📚 For Learners 💡 For Oncologists 🎯 For Hospitals Practice rare cases Sharpen decision-making Train staff effectively Safe trial & error Review protocols Reduce real- world risk Instant feedback Stay updated Cost-effective learning 🩺 Why It Matters 💬 "It’s like having a virtual cancer patient you can learn from anytime, anywhere."
Why It’s a Game-Changer for Oncology Training ✅ What It Does 🎯 Why It Matters Simulates rare cases Learn cancers you may never see in clinic Creates imaging & pathology Practice diagnosis and reporting Guides decision-making Practice tumor board discussions Allows repeated practice Learn at your own pace, safely Adaptive e-learning Get real-time feedback & quizzes
Real Case Example • 🧾 Case: 12-year-old with Ewing Sarcoma of Frontal Lobe How Generative AI Helps: • 🧠 Generates synthetic MRI showing frontal lobe mass • 🔬 Simulates pathology (CD99+, small round blue cells) • 🎯 Guides through diagnosis + treatment decisions • 📈 Tumor board simulation: surgery, chemo, RT discussion • 💬 Includes family counselling & follow-up planning
🔧 Tools in Action 🛠 Tool Application in This Case SYNTHRAD Generates CT/MRI for rare CNS Ewing cases PathAI / Aiforia Simulates digital pathology for teaching histology SimX 3D simulation of biopsy procedure and tumor excision ChatGPT MedSim Stepwise reasoning through treatment protocols Glass AI Generates evolving patient scenarios + quiz format
AI for Real-World Evidence Generation 🧠 What is RWE? • Real-World Evidence (RWE) comes from real patients in real settings — outside of clinical trials. Sources: EHRs, registries, insurance claims, wearable devices, patient-reported 🗂️ outcome 🔧 Task 🚀 AI Advantage Extracts clean data from messy EHRs NLP & ML clean up and standardize text Identifies hidden patterns Machine learning detects trends Fills data gaps (e.g., follow-up) Predictive models estimate missing info Generates research-ready datasets Saves years of manual data curation Enables faster observational studies Supports quicker decision-making 🤖 How AI Helps: 💬 “AI transforms everyday patient data into powerful evidence for better cancer care.”
What is Multimodal AI & How It Works 🧠 What It Is Multimodal AI = AI that analyzes different types of patient data together, such as: ️ 🖼️Imaging (CT, MRI) 🔬 Pathology slides 🧬 Genomics 🧪 Lab results & EHR data ⚙️How It Works 1.🧩 Collects data from different sources 2.🔗 Connects them by patient & time 3.🧠 Analyzes patterns across data 4.📊 Suggests diagnosis, treatment, risk 💡 “Like a super-intelligent tumor board that sees everything at once.”
How Doctors Use Multimodal AI 🛠️Use 💬 What It Helps With Tumor boards (e.g. Tempus One) Unified view: scan + biopsy + mutation data AI platforms (e.g. Owkin, PathAI) Predicts treatment response from mixed data Genomic + Imaging tools Finds hidden links: e.g., gene + tumor behavior EHR-integrated alerts Flags best therapy based on all patient info 🔍 Where to Access It? • Hospital tumor boards • Academic AI collaborations (MSK, Mayo, Tata Memorial) • Partner platforms: Tempus, Owkin, PathAI, Foundation Medicine
AI in LMICs (Low- and Middle-Income Countries) 🧩 Why LMICs Need AI in Oncology ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Fewer specialists, high patient load 🏥 Limited access to diagnostics & imaging 💸 Budget constraints for advanced care 🧭 Late-stage diagnosis common 🤖 What AI Brings • 📱 Mobile-based cancer screening • 🧠 Decision support for general physicians • ⚡ Faster, cheaper diagnostics • 📡 Telemedicine & remote follow-up support
AI Impact in India & Global South 🛠️Application 💡 AI Support Breast & Cervical Screening Niramai (thermal AI), Wadhwani AI (image analysis) Radiology in Rural Areas AI tools for X-ray/CT scan interpretation Pathology Access AI slide readers used where pathologists are few Radiotherapy Planning AI assists in auto-contouring, dose optimization Patient Follow-up & Navigation Chatbots & SMS reminders for remote patients 💬 “AI bridges the healthcare gap — where doctors are few, data leads the way.”
AI + Human Expertise – The Future of Oncology Care 🤖 What AI Brings: Rapid data analysis Pattern recognition Predictive modeling Workflow automation ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️What Doctors Bring: Clinical experience Contextual thinking Ethical decision-making Patient connection & trust 💡 Why It Matters •AI assists — it’s fast, tireless, and data-hungry •Doctors lead — they apply deep training, clinical insight, and judgment •Together, they ensure care is both cutting-edge and patient-centered
‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Doctor Leads 🤖 AI Supports Integrates AI insights with clinical experience Analyzes large datasets in seconds Makes the final treatment decision Suggests evidence-based options Recognizes nuances, exceptions, and patient preferences Detects patterns humans might miss Provides holistic, ethical, and personalized care Automates repetitive tasks ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Doctor = Pilot | 🤖 AI = Co-Pilot 💬 “AI is a tool — not a substitute. It learns from data, but doctors learn from patients.” ✅ Doctors remain in command ✅ AI enhances precision, not replaces decision-making ✅ Compassion + Competence + Computation = Modern Oncology AI + Human Expertise – The Future of Oncology Care
🧬 Biopsy & AI in the Modern Era: Balancing Innovation and Ethics 🔍 Why Biopsy Still Matters • Gold standard for cancer diagnosis • Confirms malignancy before major treatments • Guides molecular and targeted therapies 🤖 How AI Enhances Biopsy •Predicts suspicious areas (from imaging) •Assists in needle placement (image-guided) •Analyzes pathology slides (faster + accurate) •Suggests likely mutations before report arrives ⚖️Ethical Considerations ❓ Can AI replace histopathological confirmation? 🔐 Data privacy: AI models trained on biopsy samples 🧠 Risk of overreliance: AI vs. pathologist’s judgment 📋 Informed consent: Use of AI in diagnosis must be transparent 🗣️"AI supports the path, but biopsy lights the way."
Key Takeaways – AI in Oncology 🔬 Early Detection: AI improves cancer detection from imaging & pathology 🎯 Precision Treatment: Supports targeted therapy, radiation planning & personalization 📊 Data-Driven Decisions: Helps in prognosis, risk stratification & treatment choices 🤖 Workflow Automation: Assists in contouring, reporting & documentation 🧾 Clinical Trials: Optimizes recruitment, synthetic control arms & trial design 🏥 Follow-up & Monitoring: Tracks symptoms, alerts for recurrence, boosts compliance ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Augments Doctors: Enhances—not replaces—oncologist’s expertise ⚖️Ethical & Legal Caution: Requires transparency, validation & human oversight 🎤 "AI is not the future instead of oncologists — it’s the future with oncologists."
Challenges & Opportunities – AI in Oncology • Challenges 🏥 Data Quality: High-quality, diverse datasets needed ⚖️Ethics: Privacy, bias, and transparency issues ⏳ Regulatory Delays: Slow adoption and approvals 🤖 Integration: Resistance to change in clinical workflows ⚖️Legal Risk: Who’s accountable in case of error? Opportunities 🔬 Early Diagnosis: Faster, more accurate cancer detection 🎯 Precision Medicine: Tailored treatments based on AI insights 📊 Predictive Power: Forecasting outcomes and recurrence 🌍 Global Access: AI tools improve care in underserved areas 💼 Efficiency: Reduces workload, lowers costs
Call for Collaborative Innovation 🤝 AI + Oncology = A Game-Changer: Harnessing the power of AI to advance cancer care ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Doctors + AI: Collaboration, not competition, is key—AI enhances, not replaces, expert judgment 🌍 Global Collaboration: Join forces across disciplines, countries, and sectors to drive innovation 🚀 Continuous Learning: AI models evolve—doctors’ expertise evolves with them 🌱 Innovation Ecosystem: Collaboration among tech, healthcare, and research communities fuels breakthroughs 🧠 AI as an Assistant: Working with AI to make faster, smarter, and more personalized decisions 💬 “Innovation thrives when we combine knowledge, technology, and collaboration.”
AI & The Future of Oncology 🔮 What’s Coming Next? Hyper-personalized cancer care — from diagnosis to survivorship Real-time decision-making using live patient data Digital twins to simulate patient responses before actual treatment Predictive models to anticipate recurrence, resistance, and side effects AI-guided drug discovery speeding up new cancer therapies Equity-focused AI to bridge care gaps in low-resource settings 🤖💡 Future Vision: Doctors and AI working side-by-side — not just treating cancer, but outsmarting it. “AI won't replace oncologists, but oncologists using AI will redefine cancer care.”
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Artificial Intelligence in Oncology: Transforming Cancer Carepptx

  • 1.
    AI x Oncology: Powerof Intelligence ⚖️Black Box & Ethics 🧬 Digital Twins 🎨 Generative AI 🔍 AI & Biopsy 🧠 Multimodal AI ️ 🗂️Real-World Evidence ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Smart Tumor Boards Dr. Sumit Kumar Assistant Professor NEIGRIHMS, Shillong, Meghalaya IBM Watson for Oncology PathAI DeepMind Health (Google Health) Zebra Medical Vision Arterys Aidoc CancerIQ Tempus
  • 2.
    • Rising CancerCases: Over 20 million new cases globally expected by 2025 — too much for manual systems alone. • Data Explosion: Imaging, genomics, pathology, EHRs — more data than any human can process alone. • Need for Precision: One-size-fits-all treatment is outdated — AI helps personalize care. • Time Pressure on Oncologists: Burnout is real — AI can assist, not replace. • Early Detection & Better Outcomes: AI can spot subtle signs before humans do. • Speed + Scale: AI can analyze thousands of records/images in minutes. Why AI in Oncology? "In 2030, will you be working with AI — or watching it work instead of you?"
  • 3.
    What is AI? 🤖AI 📈 ML 🧠 DL Machines that think like humans Learn from data & improve over time Learns from large data using brain-like layers Can follow rules, make decisions Finds patterns (e.g., tumor risk) Great for images, pathology, scans Example: Chatbot, tumor board support Example: Predicting recurrence Example: Detecting cancer in CT/biopsy slides 🧠 = Think like a human 📊 = Learn from data 🧬 = Think deeply using layers "AI is the brain, ML is how it learns, and DL is how it learns really deep stuff — like cancer scans." Simple Real-Life Examples: •AI: A chatbot giving cancer information •ML: Predicting if a lung nodule is benign or malignant based on past data •DL: Recognizing cancer in pathology slides or CT scans automatically
  • 4.
    Scope of AIin Oncology 🔍 Diagnosis ️ 🖼️Imaging 💊 Treatment Planning Detects cancer early Auto-detects tumors in CT, MRI, PET Suggests personalized protocols Analyzes biopsy/pathology Segments organs/tumors Assists in radiation/chemo plans Identifies rare subtypes Reduces human error Adaptive RT planning 📈 Prognosis 🔬 Research Predicts survival/outcome Speeds drug discovery Identifies high-risk patients Simulates clinical trials Tracks recurrence risk Analyzes big datasets 🧭 Where AI Is Making an Impact “AI is now involved in almost every step of cancer care — from spotting it early to finding better treatments faster.”
  • 5.
    AI in MedicalImaging 🚀 AI Capabilities 🩻 Clinical Applications Early tumor detection CT, MRI, PET, mammography Automated organ & tumor contouring Radiotherapy planning (e.g., VMAT, IMRT) Flags suspicious lesions Lung nodules, brain mets, liver lesions Accelerates scan interpretation Faster reporting, reduced workload Enhances diagnostic accuracy Minimizes errors, improves consistency “AI in imaging acts as a second pair of eyes — faster, sharper, and tireless.”
  • 6.
    🔬 Real-World Examplesin Oncology Tool / Project Use Case Google DeepMind Breast cancer detection from mammograms Aidoc Emergency triage: flags hemorrhages, embolisms Zebra Medical Vision Detects osteoporosis, fatty liver, lung nodules Qure.ai (India-based) TB and lung cancer screening via chest X-ray Arterys MRI and CT analysis for oncology and cardiology “AI doesn’t replace radiologists — it’s like giving them super-vision. It sees faster, deeper, and often catches what we might miss.” ⚙️AI Techniques Used: •Convolutional Neural Networks (CNNs): Best for 2D/3D imaging •Radiomics: Converts images into data for AI to analyze patterns •Natural Language Processing (NLP): Reads radiology reports for trends
  • 7.
    AI in Pathology– How It Works 🧠 AI Role ⚙️How AI Does It Digital slide analysis Whole-slide images (WSI) are scanned and fed into deep learning models trained to detect cancerous regions. Pattern recognition AI uses Convolutional Neural Networks (CNNs) to spot abnormal cellular architecture, color variations, and tissue structures. Quantitative measurements AI counts cells, calculates nuclear size, mitotic activity, and other metrics faster and more objectively than humans. Predictive modeling Combines pathology images with genetic or clinical data to predict response to treatment, risk of recurrence, or survival. Workflow optimization Triage algorithms rank slides by urgency, alerting pathologists to high-risk or abnormal cases first. "Pathology is going digital — and AI is making slide reading faster, more consistent, and even predictive."
  • 8.
    Real-Life Examples ofAI in Pathology ️ 🛠️Tool / Company 📍 Use Case Paige.AI Detects prostate and breast cancer in whole-slide images with high accuracy (FDA approved) PathAI AI-powered pathology for cancer diagnosis and prognosis; used in clinical trials and research Ibex Medical Analytics AI-based detection of breast, prostate, and gastric cancers; deployed in hospitals globally Google Health Developed AI to assist in grading prostate cancer and detecting metastatic breast cancer Aiforia Cloud-based platform for AI-assisted analysis in pathology labs and education Owkin Combines pathology with genomics and outcomes to predict therapy response in trials “These tools aren’t just experimental — some are FDA-cleared and already helping pathologists in real hospitals.”
  • 9.
    AI in TreatmentPlanning 💡 AI Power 🔍 What It Does 🎯 Why It Matters 🧬 Personalized Planning Matches treatment to tumor biology & patient data Improves outcomes, avoids one- size-fits-all 🎯 Radiation Smart Plans Auto-contours, predicts dose impact Saves time, increases precision 🔄 Adaptive Therapy Adjusts plan mid-treatment Responds to real-time patient changes ⚠️Toxicity Prediction Forecasts side effects early Prevents harm, supports shared decision-making 🧠 Smart Decision Tools Recommends evidence-based regimens Supports clinician with data- backed insights "Think of AI as your co-planner — one that learns from millions of cases to help design the most effective plan for your patient."
  • 10.
    ️ 🛠️Real-World AI Toolsin Treatment Planning 🔧 Tool 🧠 Function 🌍 Used In RayStation AI-driven radiation planning & optimization Advanced radiotherapy centers Limbus AI Auto-contouring for radiation therapy North America, Europe CureMatch Personalized drug combination suggestions Precision oncology clinics IBM Watson for Oncology Evidence-based treatment recommendations Global hospitals & research Varian Ethos Adaptive radiation therapy Online adaptive RT clinics “These tools aren’t science fiction — they’re transforming real clinics, cutting planning time, and elevating care quality.”
  • 11.
    AI in Prognosis& Predictive Analytics 🧠 AI Role 📊 What It Does 🌍 Clinical Impact Survival Prediction Uses clinical, genetic, and imaging data to predict patient survival Tailors follow-up schedules, guides treatment choices Recurrence Risk Identifies high-risk patients based on tumor features and patient history Focuses treatment on high-risk groups Treatment Response Predicts how patients will respond to therapies based on data from past cases Optimizes therapy regimens Early Detection of Complications Forecasts potential side effects or complications from treatment Prevents adverse events, improves quality of life Genomic Risk Stratification Combines genetic data to determine likelihood of relapse or metastasis Enables personalized, more effective treatments "AI predicts the future — helping doctors make proactive treatment decisions." Google Health’s AI predicts breast cancer recurrence with 95% accuracy.
  • 12.
    AI in DrugDiscovery 🧠 AI Role ⚙️How It Works Target Identification Analyzes genomic & proteomic data to find cancer-driving molecules Molecule Generation Uses generative AI to design drug-like molecules (e.g., via GANs, RL) Virtual Screening Rapidly tests 1000s of compounds via computer simulation (no lab needed) Efficacy & Toxicity Prediction Trains on past trial data to forecast if a drug will work or harm Trial Optimization Matches the right patients to the right trials using EMR + AI models 🧪 Real-World Examples: •Atomwise – Uses AI to discover cancer-fighting molecules •BenevolentAI – Identifies novel drug targets in oncology •Insilico Medicine – Designed a preclinical cancer drug in < 45 days •Exscientia – AI-designed anti-cancer drugs already in trials “What used to take years, AI now does in months — it's changing the speed and precision of drug discovery.”
  • 13.
    AI in ClinicalTrials 🧠 AI Function 🔍 What It Does Patient Matching Identifies eligible patients using EMRs & genomics Trial Design Optimization Simulates outcomes to improve trial structure Site Selection Finds best trial locations based on patient access Monitoring & Safety Tracks data in real time, flags adverse events Predictive Analytics Forecasts trial success or failure early Real-World Examples: •Deep 6 AI – Finds matching patients for oncology trials in minutes •Unlearn.AI – Builds digital twins to reduce control group size •Tempus – Uses AI to optimize precision oncology trial enrollment •TriNetX – Real-world data platform for faster recruitment "AI doesn’t just speed up clinical trials — it makes them smarter, safer, and more inclusive."
  • 14.
    AI in Oncology– Limitations 🚫 Limitation 🧠 Issue Data Bias Skewed results due to unbalanced datasets Black Box Models Lack of transparency in AI decision-making Generalizability Poor performance outside training data Over-reliance on AI May reduce clinician judgment Data Quality Issues Garbage in, garbage out problem "Even the best tech can fail without careful design, oversight, and ethical guardrails."
  • 15.
    Real-World Examples: AILimitations in Oncology 📌 IBM Watson for Oncology Trained mostly on U.S. data, it provided inappropriate treatment suggestions in non-U.S. settings due to data bias. 📌 AI for Lung Cancer Detection Deep learning model performed well but couldn't explain why, leading to trust issues among radiologists. 📌 U.S. vs. Europe CT Scan Models AI trained on U.S. data struggled with European scans — a classic case of poor generalizability. 📌 AI Overuse in Skin Cancer Detection In a UK hospital, doctors began over-relying on AI outputs, ignoring clinical judgment — led to missed diagnoses.
  • 16.
    🧩 AI inOncology – Ethical Concerns ⚖️Concern 🔍 Why It Matters Patient Privacy Risk of data breaches or misuse Informed Consent Patients may not know AI is being used Accountability Unclear who is responsible for AI errors Equity & Access AI tools may not benefit all populations Regulatory Gaps Lack of clear laws and approval pathways “AI is a powerful tool — but not a perfect one. We need transparency, ethics, and constant human oversight.”
  • 17.
    🟠 Real-World Examples:Ethical Concerns in AI Use 📌 DeepMind & NHS Data Scandal (UK) AI used for kidney disease detection accessed patient data without full consent — raised privacy and transparency concerns. 📌 ER Triage Tools (U.S.) Some AI tools used to prioritize emergency cases didn't inform patients, breaching informed consent norms. 📌 Liability in Drug Dosing Errors AI recommended an incorrect dose; unclear if fault lay with software, developer, or clinician — a gray zone of accountability. 📌 AI Accessibility Gap in Rural Oncology Centers High-end AI tools were unavailable in low-resource settings, worsening healthcare disparity.
  • 18.
    AI in RadiationOncology 🤖 AI Application 💡 What It Does Auto-Contouring Automatically segments tumors & organs on CT/MRI Dose Optimization Suggests optimal radiation plans for better OAR sparing Adaptive Radiotherapy Adjusts plans in real-time based on anatomy changes Quality Assurance (QA) Detects planning or delivery errors faster Image Registration Aligns multimodal images (CT, MRI, PET) with precision 🧪 Real-World Tools: •Mirada, MIM, Limbus AI – Auto-contouring for faster planning •RayStation, Ethos (Varian) – AI-powered adaptive planning •MVision AI – Cloud-based OAR contouring •Google DeepMind + UCL – AI segmentation for head & neck radiotherapy "AI reduces hours of manual work into minutes — freeing up time for more patient-focused care."
  • 19.
    AI in MedicalOncology AI Application What It Does Treatment Decision Support AI helps oncologists choose the most effective treatment based on patient data. Genomic Profiling AI analyzes tumor DNA to identify mutations that drive cancer. Risk Prediction AI predicts cancer recurrence or metastasis based on patient history and biomarkers. Personalized Treatment AI designs custom treatment plans based on genetic and clinical data. Clinical Trial Matching AI matches patients with appropriate clinical trials based on their genomic and clinical profile. 🌍 Real-World Tools: •IBM Watson for Oncology – Personalized treatment recommendations. •Tempus – Genomic and clinical data-driven insights for precision oncology. •Foundation Medicine – Provides molecular profiling to guide personalized cancer therapy. •Guardant Health – Liquid biopsy with AI to detect actionable mutations. "AI in medical oncology enables precise and individualized treatment, ensuring patients receive the right care at the right time."
  • 20.
    AI in SurgicalOncology –🔍 Before & During Surgery AI Application What It Does Real-World Tool 3D Surgical Planning Converts imaging into 3D models for precise tumor mapping. Surgimap (Brainlab) Real-Time Navigation Guides instruments during surgery using AI + imaging. Medtronic StealthStation Tumor Detection Identifies tumor vs healthy tissue intraoperatively. PathAI with surgical microscopes Robotic Precision AI-assisted robotic arms improve accuracy & reduce errors. da Vinci Surgical System “AI is like a surgical co-pilot — mapping, guiding, and watching for trouble, all in real time.”
  • 21.
    AI in SurgicalOncology –After Surgery & Risk Management AI Application What It Does Real-World Tool Complication Prediction Identifies risk of bleeding, infection, or poor healing. MySurgeryRisk (Johns Hopkins) Recovery Monitoring Tracks vitals and recovery trends using AI algorithms. Vitals AI dashboards in smart ICUs Surgical Outcome Analysis Reviews surgery videos/data to improve future planning. Proximie AI-assisted video review Clinical Decision Support Recommends next steps post- surgery based on outcomes. IBM Watson Health (legacy platform) “AI doesn’t just end in the OR — it follows the patient, predicts risks, and improves recovery.”
  • 22.
    Clinical Decision SupportSystems (CDSS) in Oncology Function What It Does Diagnosis Assistance Suggests likely cancer types based on imaging, pathology, and clinical data. Treatment Recommendations Provides evidence-based options tailored to tumor type, stage, and biomarkers. Risk Stratification Predicts outcomes, recurrence risk, or toxicity based on patient- specific data. Clinical Trial Matching Matches patients to ongoing trials using molecular and clinical profiles. Alerts & Reminders Notifies clinicians about missing tests, drug interactions, or new guidelines.
  • 23.
    ️ 🛠️Real-World Tools &Platforms IBM Watson for Oncology – Offers evidence-based treatment recommendations (used in select centers). OncoKB + cBioPortal – Integrates genomics with treatment options. NAVIFY Tumor Board (Roche) – Multidisciplinary decision support with AI integration. Tempus – Combines clinical and molecular data to guide treatment. “CDSS doesn’t replace clinical judgment — it enhances it by turning complex data into actionable insights.” Clinical Decision Support Systems (CDSS) in Oncology
  • 24.
    AI Tools forRisk Stratification Tool What It Predicts Cancer Type CanRisk Hereditary cancer risk (BRCA, etc.) Breast, Ovarian MySurgeryRisk Post-op complications Surgical oncology OncoNPC Cancer origin (unknown primary) CUP (Cancer Unknown Primary) REMBRANDT AI Survival risk based on molecular data Brain tumors (GBM) Tempus xT Recurrence & treatment response Lung, Colon, Breast 🎤 “AI helps us stratify patients — who needs more care, and when.”
  • 25.
    Real-Time Monitoring &Adaptive Therapy AI Application What It Does Benefit Wearable Monitoring Tracks vitals, symptoms, activity in real time Early detection of complications Imaging-Based Adaptation Updates treatment plans based on tumor response Personalized RT/CT regimens Remote Patient Monitoring (RPM) Flags issues during treatment (e.g., toxicity) Prevents hospital readmissions Dose Adaptation in RT Adjusts radiotherapy dose as tumor shrinks Improves accuracy, reduces harm ️ 🛠️Real-World Tools •CureMetrix AI – Monitors imaging changes during therapy. •Varian Ethos – Real-time adaptive radiotherapy. •Biofourmis – AI for continuous home monitoring. •OncoHealth – Tracks patient symptoms during treatment. “With AI, monitoring doesn’t stop at the hospital door — it adjusts care as the patient lives it.”
  • 26.
    NLP in OncologyRecords: Extracting Data from EHRs NLP Application What It Does Benefit Clinical Data Extraction Extracts structured data from unstructured EHR notes Saves time, improves data accuracy Disease Identification Identifies cancer types, stages, and history from text Faster diagnosis & better tracking Treatment History Analyzes previous treatments, side effects, and outcomes Personalized care decisions Clinical Trial Matching Matches patients with clinical trials using EHR data Faster recruitment, targeted trials 🛠️Real-World Tools •TruCode Encoder – Converts clinical narratives into structured data •DeepMind NLP – AI for extracting clinical insights from EHRs •ClinicalBERT – NLP model for medical text classification •Oncology Data Hub – NLP for oncology-specific data extraction 🎤 “NLP transforms unstructured EHR text into powerful, actionable insights for better patient care.”
  • 27.
    AI in PalliativeCare Planning Personalized Care • Symptom Management • Prognostication AI Role What It Does Impact Symptom Prediction Identifies and predicts symptoms (e.g., pain, fatigue) Improves proactive symptom management Personalized Care Plans Customizes care based on patient data and preferences Enhances quality of life Prognostication Predicts disease progression and survival outcomes Helps with end-of-life planning Care Coordination Analyzes data to optimize team communication Streamlines care delivery 🛠️Real-World Tools •Pathfinder (Aidoc) – AI for identifying end-of-life care needs •AliveCor AI – Tracks patient vitals for palliative care adjustments •TruHealth AI – Uses AI to predict symptom exacerbations in palliative patients 🎤 “AI in palliative care helps predict needs, customize plans, and improve comfort during a difficult time.”
  • 28.
    Predictive Models forSurvival & Recurrence AI Function What It Does Impact Survival Prediction Estimates patient life expectancy based on clinical & genomic data Informs treatment goals & planning Recurrence Risk Predicts chances of cancer coming back Guides follow-up & surveillance Treatment Outcome Modeling Forecasts response to therapies Supports personalized decisions Risk Stratification Classifies patients into low/high-risk groups Enables targeted care 🛠️Real-World AI Tools •Adjuvant! Online – Survival predictions for early-stage breast cancer •PREDICT (UK) – Breast cancer survival and benefit from therapy •Tempus AI – Recurrence predictions using multi-omics data •IBM Watson for Oncology – Predicts outcomes and treatment efficacy 🎤 “AI doesn’t just react to cancer — it helps us stay one step ahead.”
  • 29.
    AI in TumorBoard Decision-Making Smarter, Faster, Unified Clinical Decisions AI Role What It Does Impact Case Summarization Extracts key data from EHRs, scans, labs Saves time, improves clarity Treatment Recommendation Suggests evidence-based options Informed, guideline-driven choices Outcome Prediction Projects survival, recurrence, side effects Personalized planning Decision Support Tools Standardizes decisions across institutions Reduces variation in care 🛠️Real-World Tools •IBM Watson for Oncology – Clinical decision support •Navify Tumor Board (Roche) – Integrates patient data for board meetings •OncoLens – AI-powered case review and workflow optimization •CureMatch – Matches genomic profiles to personalized treatment options 🎤 “AI gives tumor boards speed, structure, and sharper decisions — all backed by data.”
  • 30.
    🎯 Case Example:Recurrent Esophageal Cancer Patient scenario: • Male, 60 years, squamous cell carcinoma of the mid-esophagus • Treated with NACTRT 1 year ago • Now presents with recurrence at the primary site and new liver lesion Simplified Impact of AI • You suspect recurrence → AI highlights lesion growth and flags recurrence • You plan FOLFIRI or immunotherapy → AI shows historical response rates from global databases • You think of trial enrollment → AI finds 2 relevant trials nearby that fit this molecular profile • You want a second check → AI gives recommendation confidence levels based on published evidence 🔍 AI Doesn’t Replace — It Reinforces “I know the protocols — AI backs me up with patterns, rare options, and predictive insights.”
  • 31.
    AI in CancerFollow-up & Compliance AI supports long-term care, not just active treatment Function What It Does Why It Matters Appointment Management Reminds and reschedules follow-up visits and tests Improves continuity, avoids missed care Medication Adherence Tracks chemo/oral therapy compliance via apps and wearables Reduces treatment failure & relapse risk Symptom Monitoring Uses apps to detect fatigue, nausea, pain in real time Enables early toxicity intervention Recurrence Prediction Analyzes patterns to flag patients at higher recurrence risk Guides personalized surveillance intensity Psychosocial Support AI chatbots screen for anxiety, depression, or isolation Supports mental health & quality of life 🛠️Real-World Tools •Noona – Patient-reported outcomes in oncology •Carevive – AI-guided follow-up and toxicity alerts •CancerAid – Personal cancer care assistant •Jvion – Predictive insights on patient drop-offs & risk
  • 32.
    🎯 Follow-Up Case:Breast Cancer Survivor Status: Post-NACT Post-BCS Post-RT Now on Hormonal Therapy ➝ ➝ ➝ Phase AI Application Tool Example Benefit Post-surgery/RT monitoring Tracks symptoms: lymphedema, pain, fatigue Noona, Carevive Early detection of complications Hormonal therapy adherence Sends reminders, tracks missed doses Medisafe, Care4Today Improves compliance, reduces recurrence risk Follow-up coordination Manages imaging/test schedules Navya, CancerAid Ensures timely mammograms, labs Recurrence prediction Analyzes patient data for risk trends Jvion, Oncora AI Helps decide intensity of follow-up Mental health support Chatbots monitor emotional well-being Wysa, Ginger AI Supports quality of life, reduces distress 🔍 How AI Supports Follow-Up 🎤 “Follow-up is more than checking labs — AI makes it proactive, personalized, and patient-centered.”
  • 33.
    Case Study –IBM Watson for Oncology 🧠 What is it? AI-powered clinical decision support tool for personalized cancer care. 🏥 Used by: • Apollo Hospitals • Manipal Hospitals 🔧 How it Works: • Analyzes patient data + global literature • Recommends evidence-based treatment plans • Suggests clinical trials 📈 Pros: Speeds up decision- ✔ making Evidence-backed ✔ options Helpful in complex ✔ cases ⚠️Cons: Not always nuanced ✖ Expensive for ✖ individuals Needs constant updates ✖ "Watson helps validate my treatment plan, highlights rare protocols, and saves hours of literature review — but I still make the final call."
  • 34.
    Case Study –AI Tools for Individual Oncologists Tool Function Access OncoKB / MyCancerGenome Precision oncology info Free online CureMetrix AI for breast mammograms Demo by request Navya.ai (India) AI + expert opinion system Case submission Tempus Tumor genomics + AI insights Via patient reports Qure.ai Imaging AI (CXR, CT) License-based 💬 Use-Case Example (CA Breast): Post-NACT + BCS + RT + Hormone Therapy ➡️ AI tools can: ➡️ • Flag signs of recurrence early (imaging AI) • Track hormonal adherence (apps) • Match to clinical trials (Tempus/Trialjectory) "Even without Watson, I can use free or light tools to stay smart, scan imaging faster, and follow-up better."
  • 35.
    The Black BoxProblem – Can We Trust What We Can’t See? 🚫 What Is It? • AI gives results (e.g., “Recommend chemo”) ❌ But doesn’t explain how or why This lack of transparency is the ➡️ black box problem ⚠️Why It Matters in Oncology: • Can’t explain decisions to patients or tumor boards • Risk of errors, hidden biases • Hard to trust in rare or complex cancer cases 💬 Analogy: • “It’s like using a GPS that tells you to turn —but won’t show the map.” ✅ What We Need: • AI tools that show how they reached a decision • Better explainability = better trust in cancer care “ ️ 🗣️ In cancer care, we need clarity, not mystery. Interpretability makes AI trustworthy.”
  • 36.
    Why Is Thisa Problem in Oncology? In oncology, you need to justify every decision Situation Why Black Box is Risky Tumor board discussion You can't explain AI’s suggestion to peers Patient counseling Patient may not trust “AI says so” Legal/ethical If a wrong call is made, who’s responsible? Rare case AI may miss nuance, you won’t know why GPS Analogy AI Analogy GPS tells you “Turn left,” but doesn’t show the map or traffic AI tells you “Treat with chemo,” but doesn’t show the logic or evidence You might take a wrong turn You might make a wrong treatment decision ✅ Simple Analogy
  • 37.
    Regulatory Landscape –Who Approves AI in Oncology? 🏢 Regulator 🌍 Region 🔍 Role FDA (U.S.) USA Approves AI-based medical devices & clinical decision tools CE Mark Europe Certifies AI tools as safe & effective for clinical use CDSCO India Evaluates AI medical software (in early stages) 📜 Key Global Regulators:
  • 38.
    [Research & Development] ↓ [DataCollection & Training] ↓ [Internal Validation] (Does the AI work accurately?) ↓ [External Validation] (Tested on independent patient data) ↓ [Clinical Trials / Real-World Testing] (Does it help doctors & patients?) ↓ 🎯 Notes: •Without regulatory approval, AI should not guide treatment decisions. •FDA also introduced a Software as a Medical Device (SaMD) framework for AI. •Oncology AI tools often fall under Clinical Decision Support (CDS) rules. [Regulatory Submission] → FDA (USA) / CE Mark (EU) / CDSCO (India) ↓ [Regulatory Review] (Check for safety, effectiveness, transparency) ↓ ✅ [Approval & Certification] (AI tool can now be used in hospitals) ↓ 🏥 [Clinical Integration] (Deployed in oncology clinics & decision workflows)
  • 39.
    Validation Challenges inOncology AI ⚠️Challenge 📌 Description Data Diversity Cancer types, stages, and patient populations vary widely Small Sample Sizes Especially in rare cancers — not enough training data Overfitting Risk AI may perform well on test data but fail in real- world Changing Standards Treatment protocols evolve — AI must adapt too Lack of Gold Standard No perfect benchmark for diagnosis or outcome in some cancers 🔍 Why Validation Is Hard: 💡 Why It Matters: “An unvalidated AI tool may look smart — but can make life-altering mistakes in oncology.”
  • 40.
    📦 📦 Case Example:IBM Watson for Oncology – Validation Gap 📉 What Happened: IBM Watson was deployed in several hospitals to help oncologists suggest cancer treatments. In some cases, Watson gave unsafe or irrelevant treatment suggestions. Internal reports showed that Watson’s suggestions were based on synthetic data and limited real-world validation.
  • 41.
    AI vs HumanIntelligence – Collaboration, Not Replacement 🧠 Human Strengths: • Clinical judgment • Empathy & communication • Handling unexpected situations • Understanding patient values 🤖 AI Strengths: •Analyzing large datasets instantly •Spotting subtle patterns in scans or genes •Predictive modeling •Working 24/7 with no fatigue ⚖️The Sweet Spot: Augmented Oncology “Oncologists + AI = Faster, smarter, and more personalized care.” 💬 Analogy: “AI is the GPS, but the doctor is still driving.”
  • 42.
    Legal Implications inMisdiagnosis "When AI suggests wrong, who takes the blame?" 👤 Party 📌 Responsibility Doctor Final decision-maker; must not rely blindly on AI Hospital Responsible if unvalidated AI is deployed or training lacks AI Developer Rarely liable; unless faulty design or undisclosed risks 🔍 Who May Be Liable? ❗ Key Legal Principles: •AI = Assist, Not Replace •Human judgment is non-negotiable •Documentation & reasoning matter in court ️ 🛡️Doctor’s Defense: ✅ Used AI as decision support ✅ Followed guidelines ✅ Documented reasoning ⚠️Bottom Line: Legal accountability stays with humans. AI helps, but doesn’t excuse poor judgment.
  • 43.
    Digital Twin inOncology: “A virtual you — tested before treated.” 💡 Function 🧪 Use in Oncology Simulate treatment Predict chemo/RT response Personalize plans Tailor dosing, avoid toxicity Monitor disease Detect relapse early Trial optimization Run virtual drug tests 🤖 What is it? A digital replica of a cancer patient built from clinical data, scans, genomics & treatment history. 🔍 What Can It Do? 🌍 Real Tools: •Siemens Healthineers – RT simulation •Unlearn.AI – Synthetic trial arms “ ️ 🗣️ Digital twins = practice on the clone, perfect on the patient.”
  • 44.
    Generative AI: YourVirtual Cancer Case Trainer 🔍 Definition Generative AI = AI that creates new content like images, text, or data — not just analyze it. In oncology, it helps simulate: 🧬 Rare cancer cases 🖼️Imaging scans (CT, MRI, PET) 🔬 Pathology slides (H&E, IHC) 📋 Treatment plans & tumor board decisions 📚 For Learners 💡 For Oncologists 🎯 For Hospitals Practice rare cases Sharpen decision-making Train staff effectively Safe trial & error Review protocols Reduce real- world risk Instant feedback Stay updated Cost-effective learning 🩺 Why It Matters 💬 "It’s like having a virtual cancer patient you can learn from anytime, anywhere."
  • 45.
    Why It’s aGame-Changer for Oncology Training ✅ What It Does 🎯 Why It Matters Simulates rare cases Learn cancers you may never see in clinic Creates imaging & pathology Practice diagnosis and reporting Guides decision-making Practice tumor board discussions Allows repeated practice Learn at your own pace, safely Adaptive e-learning Get real-time feedback & quizzes
  • 46.
    Real Case Example •🧾 Case: 12-year-old with Ewing Sarcoma of Frontal Lobe How Generative AI Helps: • 🧠 Generates synthetic MRI showing frontal lobe mass • 🔬 Simulates pathology (CD99+, small round blue cells) • 🎯 Guides through diagnosis + treatment decisions • 📈 Tumor board simulation: surgery, chemo, RT discussion • 💬 Includes family counselling & follow-up planning
  • 47.
    🔧 Tools inAction 🛠 Tool Application in This Case SYNTHRAD Generates CT/MRI for rare CNS Ewing cases PathAI / Aiforia Simulates digital pathology for teaching histology SimX 3D simulation of biopsy procedure and tumor excision ChatGPT MedSim Stepwise reasoning through treatment protocols Glass AI Generates evolving patient scenarios + quiz format
  • 48.
    AI for Real-WorldEvidence Generation 🧠 What is RWE? • Real-World Evidence (RWE) comes from real patients in real settings — outside of clinical trials. Sources: EHRs, registries, insurance claims, wearable devices, patient-reported 🗂️ outcome 🔧 Task 🚀 AI Advantage Extracts clean data from messy EHRs NLP & ML clean up and standardize text Identifies hidden patterns Machine learning detects trends Fills data gaps (e.g., follow-up) Predictive models estimate missing info Generates research-ready datasets Saves years of manual data curation Enables faster observational studies Supports quicker decision-making 🤖 How AI Helps: 💬 “AI transforms everyday patient data into powerful evidence for better cancer care.”
  • 49.
    What is MultimodalAI & How It Works 🧠 What It Is Multimodal AI = AI that analyzes different types of patient data together, such as: ️ 🖼️Imaging (CT, MRI) 🔬 Pathology slides 🧬 Genomics 🧪 Lab results & EHR data ⚙️How It Works 1.🧩 Collects data from different sources 2.🔗 Connects them by patient & time 3.🧠 Analyzes patterns across data 4.📊 Suggests diagnosis, treatment, risk 💡 “Like a super-intelligent tumor board that sees everything at once.”
  • 50.
    How Doctors UseMultimodal AI 🛠️Use 💬 What It Helps With Tumor boards (e.g. Tempus One) Unified view: scan + biopsy + mutation data AI platforms (e.g. Owkin, PathAI) Predicts treatment response from mixed data Genomic + Imaging tools Finds hidden links: e.g., gene + tumor behavior EHR-integrated alerts Flags best therapy based on all patient info 🔍 Where to Access It? • Hospital tumor boards • Academic AI collaborations (MSK, Mayo, Tata Memorial) • Partner platforms: Tempus, Owkin, PathAI, Foundation Medicine
  • 51.
    AI in LMICs(Low- and Middle-Income Countries) 🧩 Why LMICs Need AI in Oncology ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Fewer specialists, high patient load 🏥 Limited access to diagnostics & imaging 💸 Budget constraints for advanced care 🧭 Late-stage diagnosis common 🤖 What AI Brings • 📱 Mobile-based cancer screening • 🧠 Decision support for general physicians • ⚡ Faster, cheaper diagnostics • 📡 Telemedicine & remote follow-up support
  • 52.
    AI Impact inIndia & Global South 🛠️Application 💡 AI Support Breast & Cervical Screening Niramai (thermal AI), Wadhwani AI (image analysis) Radiology in Rural Areas AI tools for X-ray/CT scan interpretation Pathology Access AI slide readers used where pathologists are few Radiotherapy Planning AI assists in auto-contouring, dose optimization Patient Follow-up & Navigation Chatbots & SMS reminders for remote patients 💬 “AI bridges the healthcare gap — where doctors are few, data leads the way.”
  • 53.
    AI + HumanExpertise – The Future of Oncology Care 🤖 What AI Brings: Rapid data analysis Pattern recognition Predictive modeling Workflow automation ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️What Doctors Bring: Clinical experience Contextual thinking Ethical decision-making Patient connection & trust 💡 Why It Matters •AI assists — it’s fast, tireless, and data-hungry •Doctors lead — they apply deep training, clinical insight, and judgment •Together, they ensure care is both cutting-edge and patient-centered
  • 54.
    ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Doctor Leads 🤖AI Supports Integrates AI insights with clinical experience Analyzes large datasets in seconds Makes the final treatment decision Suggests evidence-based options Recognizes nuances, exceptions, and patient preferences Detects patterns humans might miss Provides holistic, ethical, and personalized care Automates repetitive tasks ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Doctor = Pilot | 🤖 AI = Co-Pilot 💬 “AI is a tool — not a substitute. It learns from data, but doctors learn from patients.” ✅ Doctors remain in command ✅ AI enhances precision, not replaces decision-making ✅ Compassion + Competence + Computation = Modern Oncology AI + Human Expertise – The Future of Oncology Care
  • 55.
    🧬 Biopsy &AI in the Modern Era: Balancing Innovation and Ethics 🔍 Why Biopsy Still Matters • Gold standard for cancer diagnosis • Confirms malignancy before major treatments • Guides molecular and targeted therapies 🤖 How AI Enhances Biopsy •Predicts suspicious areas (from imaging) •Assists in needle placement (image-guided) •Analyzes pathology slides (faster + accurate) •Suggests likely mutations before report arrives ⚖️Ethical Considerations ❓ Can AI replace histopathological confirmation? 🔐 Data privacy: AI models trained on biopsy samples 🧠 Risk of overreliance: AI vs. pathologist’s judgment 📋 Informed consent: Use of AI in diagnosis must be transparent 🗣️"AI supports the path, but biopsy lights the way."
  • 56.
    Key Takeaways –AI in Oncology 🔬 Early Detection: AI improves cancer detection from imaging & pathology 🎯 Precision Treatment: Supports targeted therapy, radiation planning & personalization 📊 Data-Driven Decisions: Helps in prognosis, risk stratification & treatment choices 🤖 Workflow Automation: Assists in contouring, reporting & documentation 🧾 Clinical Trials: Optimizes recruitment, synthetic control arms & trial design 🏥 Follow-up & Monitoring: Tracks symptoms, alerts for recurrence, boosts compliance ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Augments Doctors: Enhances—not replaces—oncologist’s expertise ⚖️Ethical & Legal Caution: Requires transparency, validation & human oversight 🎤 "AI is not the future instead of oncologists — it’s the future with oncologists."
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    Challenges & Opportunities– AI in Oncology • Challenges 🏥 Data Quality: High-quality, diverse datasets needed ⚖️Ethics: Privacy, bias, and transparency issues ⏳ Regulatory Delays: Slow adoption and approvals 🤖 Integration: Resistance to change in clinical workflows ⚖️Legal Risk: Who’s accountable in case of error? Opportunities 🔬 Early Diagnosis: Faster, more accurate cancer detection 🎯 Precision Medicine: Tailored treatments based on AI insights 📊 Predictive Power: Forecasting outcomes and recurrence 🌍 Global Access: AI tools improve care in underserved areas 💼 Efficiency: Reduces workload, lowers costs
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    Call for CollaborativeInnovation 🤝 AI + Oncology = A Game-Changer: Harnessing the power of AI to advance cancer care ‍ ⚕️ ‍ ️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️ ‍ ⚕️Doctors + AI: Collaboration, not competition, is key—AI enhances, not replaces, expert judgment 🌍 Global Collaboration: Join forces across disciplines, countries, and sectors to drive innovation 🚀 Continuous Learning: AI models evolve—doctors’ expertise evolves with them 🌱 Innovation Ecosystem: Collaboration among tech, healthcare, and research communities fuels breakthroughs 🧠 AI as an Assistant: Working with AI to make faster, smarter, and more personalized decisions 💬 “Innovation thrives when we combine knowledge, technology, and collaboration.”
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    AI & TheFuture of Oncology 🔮 What’s Coming Next? Hyper-personalized cancer care — from diagnosis to survivorship Real-time decision-making using live patient data Digital twins to simulate patient responses before actual treatment Predictive models to anticipate recurrence, resistance, and side effects AI-guided drug discovery speeding up new cancer therapies Equity-focused AI to bridge care gaps in low-resource settings 🤖💡 Future Vision: Doctors and AI working side-by-side — not just treating cancer, but outsmarting it. “AI won't replace oncologists, but oncologists using AI will redefine cancer care.”
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