The document discusses the role of artificial intelligence in transforming the healthcare industry. It provides an overview of how AI is being applied in domains like medical imaging, diagnostics, personalized healthcare, data-driven decision making, and healthcare communication. It also describes some projects at CoDTeEM, a research group applying AI to solve local healthcare problems. Some challenges and limitations of AI in healthcare are mentioned, such as issues regarding adoption, performance, privacy, interpretability, and trust.
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Artificial Intelligence
• The practice of getting machines to mimic human intelligence
to perform tasks
• Composed of 2 words: Artificial and Intelligence
– Artificial: Anything which is not natural and created by human
– Intelligence: Ability to understand, reason and plan
• Voice assistants like Siri and Alexa,
– Customer service chatbots that pop up to help you navigate websites.
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Cognitive functions such as
perceiving, reasoning,
learning, interacting with the
environment, problem
solving, and even exercising
creativity.
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Why now?
• Recent breakthroughs made
possible by:
– Huge data sets in various fields
(image net, web-scale text data)
– Large-scale computing resources
such as cloud and GPU
– Easy-to-use software tools and
libraries (TensorFlow etc)
– Improved algorithms (including deep
neural networks)
– Heavy investment by the industry
– Media hype
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GPGPU
Heterogeneous Computing
GoogLeNet
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Role of data
Data is everywhere
• Google: processes 24 peta bytes of
data per day
• Facebook: 10 million photos
uploaded every hour
• Youtube: 1 hour of video uploaded
every second
• Twitter: 400 million tweets per day
• Astronomy: Satellite data is in
hundreds of PB
• …
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Evolution of AI in
Healthcare
• Amount of data in the medical field to double
– 1950: approximately 50 years.
– 2010: Just 3.5 years.
– 2020: 73 days.
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AI in Healthcare
• Improved Healthcare is one of the most
successful application of AI
• Through Automation, AI is poised to
broadly reshape medicine, transforming
how medical professionals diagnose, treat,
and manage diseases
• Improved the experiences of both clinicians
and patients. Improved day-to-day life
of healthcare practitioners
– spend more time looking after patients and in so
doing, raise staff morale and improve retention
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A Brief Timeline of AI in Healthcare
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• 1950: Alan Turing released a test to test for machine intelligence.
• 1956:The term “Artificial Intelligence” was first coined by the father of AI,
John McCarthy
• 1964: Eliza The first chatbot was invented by Joseph Weizenbaum
• 1971: Research Resource on Computers in Biomedicine was
founded: Saul Amarel at Rutgers University
• 1972: Development of MYCIN: Expert Rule-Based System at
Stanford University
• 1980: Development of EMYCIN: Expert Rule-Based System
• 1986: Development of Dxplain: a Decision Support System
• 2007: IBM Watson
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A Brief Timeline of AI in Healthcare
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• 2011: Siri; 2014: Alexa
• 2011: DanNet triggers deep CNN revolution
• 2012: Deep Learning wins renowned image classification
contest
• 2015: PharmaBot
• 2017: Chatbot Mandy: Automated Patient Intake
• 2017: Arterys: First FDA approved cloud-based DL-Application in
healthcare
• 2022: ChatGPT
• 2022 : IBM Watson Health (Merative)
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AI In Healthcare
Medical Diagnostic
Imaging
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Disease Detection: Detecting and Screening Vision threatening eye
diseases, heart disease, and cancer: Existing solutions are used for assisting
cancer, COVID-19 detection, etc.
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AI In Healthcare
Medical Diagnostic
Imaging
AI-assisted high-
quality
care/Patient
Centric Approach
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Disease Detection: Detecting and Screening Vision threatening eye
diseases, heart disease, and cancer: Existing solutions are used for assisting
cancer, COVID-19 detection, etc.
Proactive management of a healthy lifestyle.
Technology apps encourage healthier behavior in individuals
Mobile Apps
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AI In Healthcare
Medical Diagnostic
Imaging
AI-assisted high-
quality
care/Patient
Centric Approach
Data-Driven
Healthcare
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Disease Detection: Detecting and Screening Vision threatening eye
diseases, heart disease, and cancer: Existing solutions are used for assisting
cancer, COVID-19 detection, etc.
Proactive management of a healthy lifestyle.
Technology apps encourage healthier behavior in individuals
Mobile Apps
Data Compilation for clinical purposes
Electronic Health Reports
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AI In Healthcare
Medical Diagnostic
Imaging
AI-assisted high-
quality
care/Patient
Centric Approach
Data-Driven
Healthcare
Enhanced
Healthcare
Communication
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Disease Detection: Detecting and Screening Vision threatening eye
diseases, heart disease, and cancer: Existing solutions are used for assisting
cancer, COVID-19 detection, etc.
Proactive management of a healthy lifestyle.
Technology apps encourage healthier behavior in individuals
Mobile Apps
Data Compilation for clinical purposes
Electronic Health Reports
AI-assisted apps for chatting with doctors;
Appointment booking Better feedback by health professionals: better
understanding of the day-to-day patterns and needs of the people they care
for
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AI In Healthcare
Medical Diagnostic
Imaging
AI-assisted high-
quality
care/Patient
Centric Approach
Data-Driven
Healthcare
Enhanced
Healthcare
Communication
Robotics
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Disease Detection: Detecting and Screening Vision threatening eye
diseases, heart disease, and cancer: Existing solutions are used for assisting
cancer, COVID-19 detection, etc.
Proactive management of a healthy lifestyle.
Technology apps encourage healthier behavior in individuals
Mobile Apps
Data Compilation for clinical purposes
Electronic Health Reports
AI-assisted apps for chatting with doctors;
Appointment booking Better feedback by health professionals: better
understanding of the day-to-day patterns and needs of the people they care
for
Simple laboratory robots to highly complex surgical robots that can either
aid a human surgeon or execute operations by themselves
Suitable for repetitive tasks: rehabilitation, physical therapy and in
support of those with long-term conditions.
18. Center of Data and Text Engg and Mining
(CoDTeEM)
Local Solutions for Local Problems
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19. CoDTeEM
• A small group of researchers/experts
from academia, industry, and the public
sector to solve real-life problems using
both existing and novel AI
• Funding Grants in Healthcare (Over 30M PKR)
• Research/Academic Graduates
• Over 20 MS/PhD
• Students Over 25 UG Students
20. CoDTeEM
Project Awarding Body User
Advancing Maternal Health
through AI-Powered
Gestational Diabetes
Prediction
Canadian International Development
(IDRC) for Artificial Intelligence for Global
75k CAD (~16M PKR)
Shifa International Hospital
Medical Community in
Health Informatics and
Disease Detection
C&IT Branch, Govt of Pakistan
15.06 M PKR
Medical Directorate
AFPGMI
Personalized Smart Space for
DHH Individuals
Ignite NGIRI Deaf and Hard of Hearing
Community
An Automated Teeth Lesion
Diagnosis based on deep
learning
Collaboration Armed Forces Institute of
Natural Language Processing (Social Media)
Mental Health Illness Depression Detection Adverse Drugs Reaction
22. AI-Powered Gestational Diabetes Prediction
• Gestational Diabetes Mellitus (GDM) is a complex disease with multiple risk factors
affecting its diagnosis and outcomes.
• Patients with delayed diagnosis can have poor perinatal and postnatal outcomes for
mother and baby.
• Risk prediction ideally at the time of conception or at a first antenatal visit in the first
trimester can help in early lifestyle and dietary modification as well as can result in more
close monitoring of patients.
• GDM is prevalent in 18% -20% of pregnant ladies in the South Asian population. Study
published in Nature
• We propose to make an AI-powered predictive tool that can successfully predict a patient’s
risk of developing of GDM.
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23. AI-Powered Gestational Diabetes Prediction
• Objectives
• Implement an AI-based GDM
screening tool for both rural and
urban populations of South Asia
by selecting Pakistan as a use
case.
• The research will target pregnant
women of diverse ethnicities and
socio-economic backgrounds to
ensure equitable access to GDM
screening.
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25. Health Informatics and Disease Detection
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• Patient wise data entry form
• Provision to add multiple cases for particular patient
• Vaccination/ lab tests hist track
Disease Data Mgmt
(Extended Scope)
• GUI with time-series plots, net production numbers,
threshold-based alerts and disease distribution clusters
as map overlays (Dengue & COVID as pilot projects)
Disease Descriptive Analysis
• Compartmental Models library with foundational
models for COVID and Dengue data subsequently
extendable to other vector-borne diseases.
Disease Predictive
Analysis
26. Health Informatics and Disease Detection
TOP DISEASE/ TOP FORMATIONS FORMATION WISE DISEASE TREND
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Hearing Loss – South-East Asia
Approx more than 10 % of 109 Mn individuals in South-East Asia reside in Pakistan
31. Proposed
Solution –
Personalized
Smart Device
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End user devices:
Edge Computing
enabled output
devices to provide
visual or tactile cues
to the user
Sensors: Audio cue
collection from the
environment
Filter and Amplifier:
Removes unwanted
noise from the sound
data and amplify
relevant cues.
Machine learning
model: Model to
classify sound profiles
from the audio cues
Personalization
engine: Personalize
the sound awareness
tool to each user's
individual needs
Notification Engine :
Visual, textual and
sensory alerts using
smart device, mobile
phones, wearable etc
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Project Prototype - Basic Version Developed
Acoustic Cues from
surrounding
Visual Alerts
Alerts History/
Analytics
Acoustic Cues from
Surrounding using RaspberryPi
34. Dataset Annotation
• Five types of lesions were localized manually using bounding polygons around the carious
regions.
• The annotations were saved in a JSON file, where each mask represents a set of polygon
points
35. Introduction – Endodontic Periodontal
Lesions
• Endodontic-periodontal lesions can be classified into 5 classes:
o Primary endodontic lesions
o Primary endodontic lesions with secondary periodontal involvement
o Primary periodontal lesions
o Primary periodontal lesions with secondary endodontic involvement
o True combined lesions
• Determining the precise etiology of an endodontic-periodontal lesion can be challenging because
these lesions have similar signs and symptoms
• With the understanding that successful treatment ultimately relies on an accurate diagnosis, it is
critical that the primary etiology be identified before therapy is initiated.
38. Research Contributions
Contributions # 1
An automated deep learning based dental caries localization
and segmentation model to identify the type of periapical
lesion and localize the lesion on periapical radiographs.
Contributions #2
A lightweight MobileNet-v2 with additional layers to
enhance the performance of Mask-RCNN on small periapical
dataset.
Contributions #3
Preprocess low-resolution images to obtain better disease
diagnosis performance.
40. Mental Health
• State of well-being in which every individual realizes his or her own
potential, can cope with the normal stresses of life, can work
productively and fruitfully, and is able to make a contribution to her
or his community”.
World Health Organization
• The practice of social media analysis to look into a variety of health-related concerns has
grown substantially in previous years.
• Through numerous social networks, people are far more likely to privately share the
symptoms of their mental illness.
• These online health forums can act as a network for communicating with individuals who
have the same symptoms and offering support.
41. Description of Dataset
• .
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Subreddit No. of
Samples
Description
mentalhealth 39,373 General sadness, overthinking, upsetting scenarios, low on daily motivations and
venting posts
Depression 258,496 Specific depressive posts relating to lack of interest, motivation and focus, running
from daily errands, confusions, self harmness and sadness .
Anxiety 86,243 Related to panic attacks, overthinking scenarios, physical changes in the body and
particular anxiety attack varying from person to person.
Bipolar 41,493 Extreme mood swings, emotionally changing conditions with highs (Happy and
hypomania) and lows (Depression).
BPD 38,216 Extreme mood swings, unstable in relationships, uncontrollable emotions and self
destructive behavior.
Schizophrenia 17,506 Related to illusions, delusions, interpreting reality abnormally, hallucinations,
impaired daily functioning.
autism 7,142 Related to developmental disorder that emerges symptoms during the first three
years of life, difficulty with communication, repetitive behaviors.
• 488,472 number of samples from 228,060 users.
• The seven subreddits includes mentalhealth, depression, Anxiety, bipolar, BPD,
schizophrenia, and autism.W
44. Merative (formerly IBM Watson Health)
• provides products and services that help clients facilitate medical research, clinical
research, real world evidence, and healthcare services, through the use of artificial
intelligence, data analytics, cloud computing, and other advanced information technology
46. Merative (formerly IBM Watson Health)
• Product families offered by Merative include:
• Health Insights *
Analyze, visualize and report complex data with an integrated data warehouse and analytics portfolio.
• Micromedex *
Support informed, evidence-based clinical decision making, drug supply chain management and patient education.
• Social Program Management *
Manage health and social programs, including eligibility, benefit management and family support programs.
• Zelta (formerly Clinical Development) *
Accelerate clinical trials with a unified, cloud-based clinical data management system.
• Merge *
Build an enterprise imaging environment with a scalable, AI-enabled infrastructure that centralizes data.
• MarketScan *
Gain insights from integrated, patient-level claims data from 273 million unique patient lives.
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Challenges
• Privacy & security concerns
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Due to issues relating to protecting investment/ intellectual
property and cybersecurity risks, data and algorithms may not be
available to the public and hence, be unable to explain why
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Challenges
• Interpretability/ Uncertainty
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Many AI algorithms — particularly deep learning algorithms —
are impossible to explain.
If a patient is informed of a life-threatening diagnosis or the need
for emergency surgery, they will undoubtedly want to know why.
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Challenges
• Trust
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Empathy: The irreplaceable doctor-patient relationship. The area where
humans will always outperform AI is emotional intelligence.
• Life-Changing Diagnosis: At present, it is unlikely that you would
trust a robot or algorithm with a life-altering decision.
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Future Prospects
• Job Security – Healthcare workers
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85 million jobs will be replaced by machines with AI by the year
2025.
World Economic Forum
97 million new jobs will be created by 2025 due to AI.
World Economic Forum
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Future Prospects
• Job Restructuring
– Global economy could create 40 million new health-sector jobs by 2030 (World Health
Organization)
– A projected shortfall of 9.9 million physicians, nurses and midwives globally over the same period
– We need not only to attract, train and retain more healthcare professionals, but we also
need to ensure their time is used where it adds most value—caring for patients.
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Future Prospects
• Role of doctors
– Penetration of AI into radiology and pathology will be slow because these jobs require more than reading
and interpreting images.
– Radiologists consult with other physicians on diagnosis and treatment, perform image-guided medical
interventions such as cancer biopsies and vascular stents, discuss test results with patients, and many
other activities.
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Future Prospects
• Doctor-Patient Relationship
– AI will handle more time-consuming,
monotonous tasks,
– it will allow for doctors to focus on
other tasks such a
understanding the day-to-day
patterns and needs of their
patients.
The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the
opportunity to restore the precious and time-honored connection and trust — the human touch — between
patients and doctors.
American cardiologist, Eric J. Topol
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Future Prospects
• Handling biases
– Diverse and representative data: Collect and use diverse and
representative data during the development and training of AI algorithms
to reduce bias and ensure fair representation of all population groups.
– Diversity in development teams: Encourage diversity in AI development
teams to bring in varied perspectives and reduce the risk of unintentional
bias in the design and evaluation of AI systems.
