AI in Healthcare.pptx

Artificial Intelligence in
Healthcare
Presented By: Dr. Anik Chakraborty, JR-2
Moderated By: Prof. (Dr.) Varun Arora
Dept. of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
Copyright Dr. Anik Chakraborty

What is Artificial Intelligence (AI): The fourth industrial revolution
“It is the science and engineering of making intelligent machines,
especially intelligent computer programs” - John McCarthy
“Systems that act like humans.” - Alan Turing, Father of computer science
At its simplest form, artificial intelligence is a field, which combines computer science and
robust datasets, to enable problem-solving.
It also encompasses sub-fields of machine learning and deep learning, which are frequently
mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI
algorithms which seek to create expert systems which make predictions or classifications
based on input data.
“AI is a machine, device, robot or tool that is powered by software programs to display the
characteristic reasoning and thinking patterns of humans”
-From the book AI in Healthcare by Parag Mahajan, MD.

AI: AI works through a programming sequence in which the program’s central point
is defined
ML: It is an application/ subfield of AI that provides systems to automatically
learn from provided examples or practice data sets, and then apply this knowledge
to new data sets in order to recognize patterns and provide probable outcomes…
exhibiting human intelligence
DL: Deep Learning is a subset of machine learning, which uses artificial neural
networks (ANN) to mimic human decision-making by imbibing large data sets in
order to properly understand and analyze a concept, and then provide a meaningful
outcome.
Algorithm: An algorithm is a set of unambiguous instructions that a mechanical
computer can execute. Many AI algorithms are capable of learning from data and
can themselves write another algorithms.

AI, ML & DL Timeline

Types of AI
Based on Ability:
1. Narrow AI (Weak AI)
2. General AI (Strong AI)
3. Super AI
Based on Functionality:
1. Reactive machines
2. Limited memory
3. Theory of mind
4. Self-awareness

The growth will be EXPONENTIAL!

Based on Functionality:

Introduction to AI in healthcare
• A paradigm shift, a game changer

Market size
• 10 BN USD by 2024 with a CAGR of 40%
• Main contributing sectors:
i. Medical imaging and diagnosis
ii. Drug discovery
iii. Therapy planning
iv. Hospital workflow
• ˃21 BN USD by 2026 with a CAGR of ˃40%

• What physicians do is algorithm based diagnosis
• Application of AI has been inevitable in this regard

Diagnostic algorithm

The inevitability
• The medical industry generates a tremendous amount of data every day.
• The use of technology to stratify and centralize this data to ensure easy accessibility
has been the need of healthcare professionals for a very long time
• Data available in the form of Electronic Health Records (EHR), X-rays, ECGs and Lab
reports can be entered into algorithms for the training of AI models
The current healthcare system and it’s effect on patient welfare
1. There just aren’t enough hours in the day- Time management for physicians
• There’s a serious dearth of quality time for a patient
• Doctor’s are overburdened by the work
• It directly affects quality of care, doctor-patient relationship and patient satisfaction
• Recording and documentation is a major part and increasing day by day

2. Absence of real time patient information: 2020 and Telehealth
• COVID pandemic has been a turning point on how doctors and patients interact.
Telemedicine has been a necessity and become a trend
• Telehealth and beyond: We have become more health conscious too. More and more
patients are opting for wearable digital health technology to monitor and evaluate their
health matrices
• This includes: Smartwatches, Fitness bands, BP and HR monitors, Pulse oximeters,
Glucometers.
• These captures real time patient information
• Many of these applications were backed by AI technologies
• If all these data can be captured and fed in a system which are accessible to patients as
well as treating physicians, the treatment can be more holistic and less time consuming

3. EHRs and beyond
• Introduction of Electronic Health Records (EHR) undoubtedly has created an advantage
over paper-based systems that were prone to errors and lapses in communication and
continuity of care
• With enhanced patient data security and ease with which reports and patients information
can be transferred from one physician to another, patients are protected from unnecessary
testing, duplication of diagnostic testing and medication errors.
• However, accuracy of the EHR data is largely dependent on physicians who enter the data
themselves through charting, round notes or handoff notes.
• Applying AI algorithms is necessary to wade through overwhelming data sets and arrive
at actionable conclusions to improve care delivery. Otherwise it is very easy to lose sight
of the objective and drown in data.

4. The abundance of information and physician limitation
• According to Journal of the American Medical Library Association, every month over
7000 articles are published in primary care journals alone
• It is not only hard but almost impossible to be up-to-date with recent advancements in a
single medical field
• The concept of evidence based medicine solely rely upon this.
• So, there will be chance of misdiagnosis and incomplete treatment for patients and more
medicolegal cases for physicians
• AI program already outperformed human researchers when given task of combing through
vast medical literature databases to derive results for focused themed researches.
• This shows the potential of AI in analyzing and filtering required information to facilitate
improved medical education.

AI in different fields of
healthcare
AI in Preventive Healthcare
• Preventive medicine has garnered lot of importance and interest in recent times
• AI algorithms are revolutionising preventive healthcare with wearables and other devices in real
time basis
IBM Watson
• Watson and oncology may one day become synonymous
• Watson was found to be concordant with the tumour board recommendations
in 90% of CA Breast cases
• With the multidisciplinary tumour board from India, Watson was concordant in
96% for Lung Ca, 81% for CA Colon and 93% for Rectal CA

Healint
• Healint actively monitors your health. Mainly ‘Migraine Buddy’ function that tracks migraine,
triggering factors and keeps a diary
• The user can also record the symptoms they get from their medication which are sent to his doctor.
Providers can then proactively adapt the treatment for such ailments

Other points
• Better data management: AI will have access to billions of EMR/ EHRs, scientific data and
other databases and will be able to find patterns and connections that humans will overlook.
Fusing AI with Blockchain technologies will further enhance data management.
Ex- DeepMind Health, Doc.ai
• Faster online consultation: The greatest asset AI brings to preventive healthcare is
providing people with medical aid anywhere, anytime.
Ex- Babylon (An app that offers online consultation based on individual medical records and
medical databases)

AI in Public Health
• In India, few of the initiatives undertaken by the central government to use AI in public
health are Imaging Biobank for cancer, for which the NITI Aayog with Department of
Bio-Technology (DBT) aims to build a database of cancer-related radiology and pathology
images of more than 20,000 profiles of cancer patients with focus on major cancers
prevalent in India.
• NITI Aayog is also working on using AI for early detection of diabetic retinopathy.
• The role of AI in tackling COVID-19 and
developing vaccines has been tremendous
• AI can be used, being used in disease surveillance

• Big data and AI is being used in public health to investigate and predict disease outbreak as well as
for the early intervention of it.
• In epidemiology, the huge potential of Big Data and AI was illustrated by a pioneering study
reported by Deiner, who demonstrated that the spread of epidemics can be detected early by
tracking online queries on disease symptoms using social media such as Google Search and
Twitter
• Pattern recognition and data analytics were used as tools
for the detection, recognition, and classification of
patterns of disease relating to the incidence of
conjunctivitis. The study results suggested that early
warning and detection of biosecurity threats and epidemics
of influenza may be possible by the surveillance of online
queries.

Application of AI in COVID-19 management
AI is being successfully used in the identification of disease clusters, monitoring of cases, prediction
of the future outbreaks, mortality risk, diagnosis of COVID-19, disease management by resource
allocation, facilitating training, record maintenance and pattern recognition for studying the disease
trend. It’s applications are
• Prediction and tracking: Bluedot identified a cluster of pneumonia cases and predicted the
outbreak and geographical location of the COVID-19 outbreak based on available data using
machine learning. HealthMap collects the publicly available data on COVID-19 and makes it
readily available to facilitate the effective tracking of its spread.
• Contact tracing: AI can augment mobile heath applications where smart devices like watches,
mobile phones, cameras and range of wearable device can be employed for diagnosis, contact
tracing and efficient monitoring in COVID-19

• Monitoring of COVID-19 cases: AI techniques are applied for monitoring
patients in clinical settings and prediction of course of treatment. Based on
the data derived from vital statistics and clinical parameters, AI may provide
critical information for resource allocation and decision-making by
prioritizing the need of ventilators and respiratory supports in the ICU
• Early diagnosis: AI was used for the detection and quantification of
COVID-19 cases from chest x-ray and CT scan images. Researchers have
developed a deep learning model called COVID-19 detection neural
network (COVNet), for differentiating between COVID-19 and community-
acquired pneumonia based on visual 2D and 3D features extracted from
volumetric chest CT scan, thus, reducing the burden for medical practitioners
& HCWs

• Development of therapeutics: AI techniques can
boost and complement traditional technologies by
reducing the time required in bringing a drug from
bench to bed by speeding up lead discovery, virtual
screening and validation processes by a huge margin
• Development of vaccines
• Curbing spread of misinformation

AI in Radiology
• Till now the most useful applications of AI in healthcare has been in the field of radiology
• This is the field where technology and computers have been involved for a long time. So it is of
no surprise that this field is the most affected by AI
• AI is now analyzing X-rays, CT scans, MRIs and the like for abnormal readings, just like a
radiologist.
X-ray of a hand by automatic calculations of
bone age by software

Enlitic: A San Francisco based startup used deep learning (AI) algorithm to integrate with
PACS (Picture Archiving and Communication Systems) in 80 clinical imaging centers
throughout central and SE Asia and Australia. In 2016, they proved that their AI can
improve the detection accuracy of radiologists by 20% in search of extremity fractures.
Arterys: With machine learning integrated in it’s 4D Flow software, it is revolutionizing
Cardiac MRI studies to overall visualization and quantification of blood flow. [Granted
USFDA clearance in Nov ‘16]
A similar start-up is Cardio DL, using cloud based image processing technology.
RAD-Logics: Silicon valley based company which is using AlphaPoint software, a
program using ML in preliminary findings, such as how a doctor would use medical records
information and analyse the images from a radiology report.

Qure.ai

• Presently AI is heavily impacting PACS, especially for tasks that are prone to human
error. Such as detecting lung nodules (Lung CA) on X-rays or bone metastasis on CT scans
• AI will not only use ML and DL technology to detect a pathology but also have the access
to individual’s previous health data and other relevant information. So it will correctly
analyze and holistically evaluate a medical condition. It will also look at previous patient
records, procedures, lab results and pathology reports from similar cases and thus will
provide best management outline.

AI in Pathology
How it’s actually done: Existing images are fed to CNN (Convoluted Neural Network, a part of
DL technology) in Training phase and then novel images are tested in Inference phase. Then their
accuracy is measured compared to standard diagnosis.

Whole Slide Imaging (WSI): WSI, also commonly referred to as “virtual
microscopy”, aims to emulate conventional light microscopy in a computer-generated
manner. WSI consists of two processes. The first process utilizes specialized hardware
(scanner) to digitize glass slides, which generates a large representative digital image
(so-called “digital slide”). The second process employs specialized software (i.e, virtual
slide viewer) to view and/or analyze these enormous digital files.
WebMicroscope: The company developed online microscopy software that allows users
to store, share and evaluate a massive collection of digitized microscope samples.
Pathologists, pharmacologists and medical researchers can access this information and
work together to manage data and discuss results.
Recent update—WebMicroscope now has included AI and ML capabilities that allowed
for the automation of tasks like tissue classification and object quantification (a task that
used to take hours for humans to perform now takes minutes)

AI in Surgery
• Experts predict that AI will do for surgeries in the 21st century what the stethoscope did
hundreds of years ago
• AI offers precisely accurate surgical robots and finely tuned diagnostic algorithms to solve
various complex surgical and clinical problems
• A surgical robot is dexterous, fatigue and tremor-free device which makes it perfect for
surgeries
• Robotic surgery also may reduce blood loss and hospital stay.

The most related applications of AI in surgical robots are
• Automation of suturing: Raven robot, PR2 robot; They have been tested with 87% successful
suturing accuracy
• ML for evaluation of surgical skill
• ML for improved surgical robotics
• ML for surgical workflow modelling: Pre, Intra and Post- operative procedures

DCNN= Deep Convolutional Neural Network; LfD= Learning from Demonstration RL= Reinforcement Learning

AI in Anesthesia
• Decision Support System: Smart monitors
• Automated assist devices: Semi automated anaesthesia
- Closed loop system
- Mc Sleepy
• Procedure training by VR
Closed Loop Anesthesia Delivery:
It is an automated control system using the feedback principle.
o Pharmacological robots
o Integrates expert systems
o Determines right drug/ right dose/ right time
o Feedback principle

Mc Sleepy
o Controlling parameters: BIS+ ANALGOSCORE+ TOF
o 3 phases (induction/ maintenance/ emergence)
o 3 infusion pumps connected to a controlling unit
o Safety measures
Sedasys
o A computer assisted personalized sedation system
o Mild to moderate propofol sedation to be delivered by non-anesthesiologists
o Consists of a full monitoring array

Kepler intubation system
o Consists of Robotic arm, Joy stick, Pentax AWS video
laryngoscope & Software control system
VR based regional anesthesia simulator

AI in Pharma industry
• From the moment of discovery to proper execution of a drug molecule, it takes 12+ years and 2.8
Billion USD on an average at present
• AI and ML can screen through hundreds of potential candidates and select which have the most
potential. It can bring down production time and cost significantly
• Pharma giants like Johnson & Johnson, Merck, Roche, GSK are investing billions in AI

• The best use cases for AI in pharma industry are drug discovery, drug manufacturing,
diagnostic assistance, and optimizing medical treatment processes.
• The tech can also help with the repurposing of new drugs, especially during the COVID-19
pandemic. AI and machine learning algorithms are able to identify molecules that may have
failed in clinical trials and predict how the same compounds could be applied to target other
diseases.

AI in dermatology
• Content Based Image Retriever (CBIR)
o DermEngine’s Visual Search
o Skin Vision
This software focuses on identifying skin cancer images
and gives medical professional visually similar pictures
with the top diagnosis and the risk of malignancy of
these past cases
Skin10: An app developed to help dermatologists diagnose skin condition. It’s
algorithm is built on an extensive database of skin condition.

Artificial Intelligence in Ophthalmology tends to
focus on imaging, and the automated
interpretation of Fundus photographs and Optical
Coherence Tomography (OCT) scans.
Other areas of interest include patient
management, disease risk prediction, progression
analysis, and automated interpretation of non-
imaging modalities (e.g. visual fieldtests)
FOCUS ON IMAGING
AI in Ophthalmology

EYE CARE IS PRIMED FOR MACHINE LEARNING
Plentiful data
Accessible organ, non-intrusive
image acquisition (2D + 3D)
Used at all levels of Healthcare Systems
Imaging services offered across
primary, secondary, and tertiary care
Tele-medicine familiarity
Clinicians already familiar with
remote diagnosis and decision tools
Existing use-cases
AI can be integrated in existing
multi-graders / triage workflows

Tong, Y., Lu, W., Yu, Y. et al. Application of machine learning in
ophthalmic imaging modalities. Eye and Vis 7, 22 (2020).
Most research efforts are focusing on the diagnosis of
conditions such as Diabetic Retinopathy andGlaucoma.
Many studies claim to match or exceed expert performance
though high inter and intra-variability betweenclinicians
makes it difficult to ascertain.
Google AI

AI in Health insurance industry:
• AI system can help in coining customized insurance plans for patients of chronic disease
• ML algorithms can augment the expertise of case managers in claiming process
• It can help in fraud detection, minimize healthcare burden and predict investment
outcomes

Role of big corporations in
Healthcare AI
IBM Watson:
• Faster access to data and knowledge
• Recommendation for better treatment
• Cloud based technology
• Health bots: Watson Assistant for Citizens (chatbot)
• Watson care manager: Personalized plan for optimal
treatment
• Future plans of Watson:
i. Drug discovery process
ii. Care management
iii. Cancer treatment
iv. Clinical trials

Google & DeepMind

Facebook
• AI for fighting suicide epidemic
• Saving people from drug addiction
Microsoft
• Healthcare NexT (2017): AI & Cloud computing platform
• AI network for healthcare, especially cardiologists
• Project EmpowerMD: To listen and learn from human doctors and automate tasks
• Microsoft healthcare bot service: Providence and Walgreens, largest health systems in USA
is using Azure-powered AI chatbot named Grace to answer their patients online
• Project InnerEye: Using ML for automatic delineation of tumors
• Microsoft Genomics

Amazon
• Amazon Comprehend Medical (ACM): It is a fully managed NLP service that can analyse
and organise data from unstructured medical notes and prescriptions. It can comprehend
medical language, anatomic terms, differential diagnoses, test reports, treatment options,
medication strength, dosage and frequency.
• Amazon HealthLake: Launched by AWS, it is a HIPAA [Health Insurance Portability and
Accountability Act] compliant platform that allows healthcare organizations to smoothly
store, analyze and transform data in the cloud.
Apple
• Apple watch: ECG recording; Launched SPO2 monitoring during COVID pandemic. Fitness
tracking elements like total steps, running pace calorie burned etc.
• Apple health app: Amount of physical activity, time spent on phone, calorie intake, and
sleep.

• In India, AI is being employed through chatbots such as Wysa that provide mental health
support.
• The Manipal Group of Hospitals has tied up with IBM’s Watson for Oncology to aid
doctors in the diagnosis and treatment of 7 types of cancer.
• Aravind Eye Care Systems is presently working with Google Brain, after previously
helping Google develop its retinal screening system by contributing images to train its
image parsing algorithms.
BeatO
• Yash Sehgal and Gautam Chopra, started BeatO in 2015. Since then, their platform has
expanded to serve around 50,000 patients across 1,500 cities.
• The functioning of the app is simple, it comes with a glucometer, which can be plugged
in to a smartphone to take reading. The reading is then saved in the app and can be used
for further guidance and intervention in case of an emergency.
State of AI in Healthcare in India

AADAR
• Healthcare starts with preventive care, which basically includes keeping a check on various
health conditions and taking proactive steps to nip diseases in the bud.
• Mumbai-based AADAR is one such venture operating in the “Ayurveda-inspired”
preventive healthcare space.
• Founded by IIT-B alumnus Aadil Shah, AADAR offers herb-based products to curb lifestyle
ailments like protein deficiencies, blood sugar, indigestion, cholesterol, and obesity.
• AADAR plans to expand into the space of menstrual wellness, skincare products, and health
supplements.
DRiefcase
• This brings us to DRiefcase, an idea hatched from the brains of IIM-B alumni Sohit
Kapoor and Harsh Parikh.
• Founded in May 2016, DRiefcase has one simple objective - to digitize personal health
records of a person and provide users with a single-point, easy-to-use access to medical
data. All this at the tap of the screen.

Challenges

Survey among consumers

Pros vs Cons of AI in healthcare

Conclusion
• AI is changing every nook and corner of healthcare
• It has it’s own challenges because health is a unique domain
where no compromise can be made.
• Like it or hate it, you can certainly not ignore AI
• So, an overall understanding is necessary to assess the reality
and prepare ourselves

Conclusion
• AI can be used as a supporting friend to the overburdened healthcare
sector
• AI has a huge role to play in precision medicine, personalised
medicine
• AI can help in analysing huge volume of health data and give us
valuable insights and trends. It can help us making data-driven
decisions
• Humans are irreplicable provided they adapt to the change and have
the necessary empathy a healthcare staff should possess. Because
that’s the only frontier left for us against algorithms and machines

Thank you

AI in Healthcare.pptx

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