Merits and Demerits of Artificial Intelligence in Health

1. ARTIFICIAL INTELLIGENCE AND ITS MERITS AND
DEMERITS FOR HUMAN HEALTH
Presented by – Dr. Bibhukant Jena (PG 1st Year)
Moderated by – Dr. Shaswati Sahu (Assistant Professor)
Dr. Pragyna Paramita Das (Tutor)
Department of Community Medicine
SCB Medical College & Hospital, Cuttack

2. INTRODUCTION
• Artificial Intelligence (AI) refers to the ability of machines to perform
cognitive tasks like thinking, perceiving, learning, problem solving and
decision making.
• It is sometimes called as machine intelligence.

3. TERMINOLOGIES

4. TERMINOLOGIES Contd..
• Data Mining – practice of examining large existing databases in
order to generate new information.
• Cloud Technologies – It refers to the use of a network of remote
servers to store, manage, access and process data rather than a
single PC or hard drive.

7. AI IN PUBLIC HEALTH
• Role of AI in Ayushman Bharat Digital Mission (ABDM) –
o The current ability to digitally identify individuals, physicians, and healthcare
facilities, enable electronic signatures, guarantee non-repudiable contracts, make
paperless payments, securely store digital records, and make contact with people,
opens up possibilities for streamlining healthcare information through digital
management.
• Imaging Biobank for Cancer - 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.
• NITI Aayog, is closely working with Microsoft and the medical start-up Forus
Health to develop automated solutions for early detection of diabetic
retinopathy.

8. NDHM ECOSYSTEM

9. AI IN PUBLIC HEALTH Contd..
• Maharashtra government has signed MoU with NITI Aayog to unveil the International
Centre for Transformational Artificial Intelligence (ICTAI), focusing on rural healthcare.
• Wadhwani AI is an official AI partner of Central Tuberculosis (TB) Division, developing
various interventions related to the TB patient care and helping India’s National TB
Elimination Programme (NTEP) become AI-ready.
• Telangana state government has launched Covid-19 live monitoring App developed by
Vera Smart Healthcare for Telangana.
• The Karnataka government launched “Healthcare Pods” developed by the Bangalore-
based firm Vevra. These pods are innovative movable hospitals integrated with AI and
help in containment of contagious diseases such as Covid-19 and TB.
• Kerala government has launched AI based course “AI Machine Learning Developer
Programme” aimed at creating new-age professionals who can fill the demand in areas
of data science, AI, among others.

10. VEVRA PODS WADHWANI AI

11. AI IN PUBLIC HEALTH Contd..
• ePaarvai -
o A smartphone app created by Tamil Nadu e-Governance Agency (TNeGA) based
on AI for combating Cataract.
o The app may be used to perform a quick eye screening by just clicking a photo.
Additionally, macular disintegration can be found using the application.
• 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.
• Many top health-based leading AI start-ups such as Netmeds, Innovaccer, Tata
Health, PharmEasy, HealthifyMe, Practo, NIRMAI, HealthPlix, Lybrtae,
HealthKart, among many others, offering healthcare products and services
related to care management and patient engagement via using virtual networks.

12. BIG DATA AND AI

13. APPLICATION OF AI IN COVID-19 MANAGEMENT
• Prediction and tracking -
oBluedot helped in identifying a cluster of pneumonia cases and predicted
the outbreak and geographical location of the COVID-19 outbreak based on
available data using machine learning.
oHealthMap 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.

14. APPLICATIONS IN COVID-19 Contd..
• Monitoring of COVID-19 Cases - AI techniques are being applied for monitoring
patients in clinical settings and prediction of course of treatment.
o 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.
o COVNet - A DL model called COVID-19 detection neural network, for
differentiating between COVID-19 and Community Acquired Pneumonia based on
visual 2D and 3D features extracted from volumetric chest CT scans.

15. HealthMap COVID TRACKING

17. AI IN HEALTHCARE IN INDIA
• Wysa - AI Chatbot 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 – It is a smartphone glucometer, which can be plugged in to a smartphone
to take reading and is then saved in the app.
• Driefcase – Online app and portal which digitizes personal health records of a
person and provide users with a single-point, easy-to-use access to medical data.

18. AI IN HEALTHCARE IN INDIA Contd..
• NIRAMAI Health Analytix -
o AI based medical imaging device to detect breast cancer - non-contact, non-
invasive, and non-radiation-based.
o NIRAMAI is harnessing AI and ML to enable early identification of breast cancer
and subsequently to increase survival rates from the disease.
• InstaECG And InstaEcho – Introduced by Tricog, a startup, which is aided by cloud
connected ECG and Echo machines and in-built algorithms that receive, interpret
and send back analysis.

19. VARIOUS APPLICATIONS OF AI IN HEALTHCARE
WORLDWIDE
• A decision support system known as DXplain was developed by the university of
Massachusetts in 1986, which gives a list of probable diagnoses based on the
symptom complex and it is also used as an educational tool for medical students.
• Germwatcher is a system developed by the University of Washington to detect
and investigate hospital acquired infections.
• An online application in UK known as Babylon can be used by the patients to
consult the doctor online, check for symptoms, get advice, monitor their health,
and order test kits.
• AI-therapy is an online course that helps patients treat their social anxiety using
therapeutic approach of cognitive behavior therapy. It was developed from a
program CBTpsych.com at University of Sydney.

20. VARIOUS APPLICATIONS OF AI IN HEALTHCARE
WORLDWIDE Contd..
• The Da Vinci Robotic Surgical System developed by Intuitive Surgicals has
revolutionized the field of surgery especially urological and gynecological
surgeries. The robotic arms of the system mimics a surgeon’s hand movements
with better precision and has a 3D view and magnification options which allow
the surgeon to perform minute incisions.
• Since 2018, Buoy Health and the Boston Children’s Hospital are collaboratively
working on a web interface-based AI system that provides advice to parents for
their ill child by answering questions about medications and whether symptoms
require a doctor visit.
• The National Institute of Health (NIH) has created an AiCure App, which
monitors the use of medications by the patient via smartphone webcam access
and hence reduce non-adherence rates.

21. DA VINCI ROBOTIC SURGICAL SYSTEM

22. VARIOUS APPLICATIONS OF AI IN HEALTHCARE
WORLDWIDE Contd..
• Fitbit, Apple, and other health trackers can monitor heart rate, activity levels,
sleep levels, and some have even launched ECG tracings as a new feature.
• The Netherlands uses AI for their healthcare system analysis - detecting mistakes
in treatment, workflow inefficiencies to avoid unnecessary hospitalizations.
• IBM’s Watson Health will be equipped to efficiently identify symptoms of heart
disease and cancer.
• Healthcare conversational projects analyzes how Siri, Google Now, S Voice, and
Cortana respond to mental health, interpersonal violence, and physical health
questions from mobile phone users allowing patients to seek care earlier.
• Molly is a virtual nurse that is being developed to provide follow-up care to
discharged patients allowing doctors to focus on more pressing cases.

23. AI IN RADIOLOGY
• CAD (Computer-Assisted Diagnosis) in screening mammography
• AI could assist in labeling abnormal exams and also by identifying quick negative
exams in CT Scans, MRI’s and X-rays.
• AI is heavily impacting Picture Archives and Communication Systems (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 and
thus will provide best management outline.

24. AI IN PATHOLOGY
• 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;
o The first process utilizes specialized hardware (scanner) to digitize glass slides,
which generates a large representative digital image (digital slide).
o The second process employs specialized software (virtual slide viewer) to view
and/or analyze these enormous digital files.
• WebMicroscope – It is an online microscopy software that allows users to store,
share and evaluate a massive collection of digitized microscope samples.
o Pathologists, pharmacologists and medical researchers can access this
information and work together to manage data and discuss results.

25. SCREENSHOT OF WebMicroscope
WHOLE SLIDE IMAGING

26. AI IN SURGERY
• 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.
o Automation of suturing: Raven II Surgical robot, PR2 robot - They have been
tested with 87% successful suturing accuracy.

27. RAVEN II SURGICAL ROBOT
PR2 ROBOT

28. AI IN ANAESTHESIA
• Closed Loop Anesthesia Delivery System (CLADS) -
o It is an automated intravenous infusion system using feedback principle.
o It delivers propofol based on patients’ frontal cortex electrical activity (EEG) as
determined by bispectral index (BIS) feedback.
• McSleepy -
o A system which administers drugs for general anaesthesia and monitors their
separate effects completely, automatically with no manual intervention.
o Measures 3 separate parameters on an IMA (Integrated Monitor of Anaesthesia)
– depth of hypnosis via EEG, pain via Analgoscore (pain score), muscle relaxation
via phonomyography.

29. AI IN ANAESTHESIA Contd..
• Sedasys -
o A computer assisted personalized sedation system.
o Mild to moderate propofol sedation can be delivered by non-anesthesiologists.
o Consists of a full monitoring array.
• Kepler Intubation System –
o It is a robotic intubation system.
o Consists of robotic arm, joy stick, Pentax AWS video laryngoscope & software
control system.

30. SEDASYS
McSleepy INTERFACE
KEPLER INTUBATION SYSTEM

31. AI IN DERMATOLOGY
Content Based Image Retriever (CBIR) -
• DermEngine’s Visual Search – An intelligent dermatology software used for the
imaging, documentation and analysis of skin conditions including skin cancer.
• Skin Vision - This software focuses on identifying skin cancer images and gives
medical professional visually similar pictures with the diagnosis and the risk of
malignancy of past cases.
• Skin10 - An app developed to help dermatologists diagnose skin condition. It’s
algorithm is built on an extensive database of skin conditions.

32. AI IN OPHTHALMOLOGY
• AI 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 field tests)

33. AI IN PHARMACEUTICAL INDUSTRY
• AI is being used 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. AI and ML 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 and ML can screen through hundreds of potential candidates during drug trials
and select which have the most potential. It can bring down production time and
cost significantly.

34. AI IN PHARMACEUTICAL INDUSTRY

35. AI IN CLINICAL TRIAL RESEARCH
• Artificial Intelligence is helping and shaping clinical trial research.
• It can be used for remote monitoring and real time data access for increased
safety.
• Example – Monitoring of biological and other signals for any sign of harm or
death of participants.

36. AI IN HEALTH INSURANCE INDUSTRY
• AI 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.

37. ChatGPT IN HEALTHCARE
• ChatGPT is a remarkable language model running on Generative Pretrained Transformer
(GPT) and delivers human-like text in real-time seamlessly.
• Its DL algorithms allow ChatGPT to analyze large amounts of complex datasets and
generate relevant responses in accordance with the user inputs provided.
• Applications –
o Improving healthcare access
o Enhancing diagnostics
o Personalized healthcare support
o Summarization of medical papers for improved medical experience
o Enhanced clinical decision support
o Biomedical research
o Medical education

38. CHALLENGES OF ChatGPT
IN HEALTHCARE
• Lack of real time data.
• Potential for ChatGPT to reinforce existing biases and inequalities in healthcare.
• Risk of misdiagnosis or incorrect treatment recommendations.
• Data breaches and unauthorized access to private medical data are possible.
• Concern about the potential for ChatGPT to replace human decision-making in
healthcare.

40. ROLE OF BIG CORPORATIONS IN
HEALTHCARE AI
• IBM Watson -
o Faster access to data and knowledge.
o Recommendation for better treatment.
o Cloud based technology
o Health bots: Watson Assistant for Citizens (chatbot)
o Watson Care Manager: Personalized plan for optimal treatment
o Future plans of Watson -
1. Drug discovery process
2. Care management
3. Cancer treatment
4. Clinical trials

41. ROLE OF BIG CORPORATIONS IN
HEALTHCARE AI Contd..
• Facebook -
o Is using AI for fighting suicide epidemic.
o Saving people from drug addiction.
• Microsoft -
o Healthcare NexT (2017) - Personalised and instant access to medical records to help
reduce data entry efforts, triage sick patients efficiently and automate outpatient care.
o Project EmpowerMD - To listen and learn from human doctors and automate tasks.
o 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.
o Project InnerEye - Research based AI-powered software tool for radiotherapy planning.
o Microsoft Genomics - Azure powered genome analysis pipeline.

42. ROLE OF BIG CORPORATIONS IN
HEALTHCARE AI Contd..
• Amazon -
o 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.
o 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 -
o Apple Watch - ECG recording; Launched SPO2 monitoring during COVID pandemic.
Fitness tracking elements like total steps, running pace calorie burned etc.
o Apple Health App - Amount of physical activity, time spent on phone, calorie intake, and
sleep.

43. SMART WEARABLES FOR HEALTH TRACKING

44. CHALLENGES OF AI IN HEALTHCARE
• Privacy Issues - Patient data contains highly sensitive personally identifiable information
(PII) (e.g., medical histories, identity information, payment information). The large data
requirements of most AI models and hospitals concerns over the possibility of data
leakages reduce the adoption of healthcare AI technologies.
• Accountability, Transparency, and Explainability - Due to the lack of transparency and
explainability associated with machine learning, it might be difficult or impossible to
understand why an algorithm made a certain conclusion.
• Bias and Inequality - There are risks involving bias and inequality in healthcare AI. AI
systems learn from the data on which they are trained, and they can incorporate biases
from those data.
• Security and Cyber security - As AI becomes increasingly used to assist in the execution
of cyber-attacks, AI software could be hacked, and the data it uses can be changed or
manipulated.

45. CHALLENGES OF AI IN HEALTHCARE Contd..
• Training - As healthcare is getting digitized, the medical curriculum has not kept
pace with introducing medical students or residents to new technologies such as
AI, mobile healthcare applications, and telemedicine. There is a need to establish
a framework where digital concepts are tested as part of the entrance
examinations and training on the use of technologies is part of the clinical
program.
• Standardization - Use of AI in healthcare is impacted by the liability for the
predictions of an algorithm. There can be wide interpatient variability in terms of
available data elements. This interpatient variability leads to missing data.

46. ADVANTAGES & DISADVANTAGES OF AI IN HEALTHCARE
ADVANTAGES DISADVANTAGES
Better data-driven decisions Concerns regarding privacy & security
Increased disease diagnosis efficiency Lack of curated healthcare data
Treatment time reduced to half High initial capital investment
Integration of information Lack of interoperability
Reduction of unnecessary hospital visits Reluctance from staff to embrace AI
Creation of time-saving administration
duties
Potential for increased unemployment
Increased time on critical cases Lack of human touch, empathy,
emotional intelligence

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