1. Artificial Intelligence
and Medicine
Dr.T.V.Rao MD

2. Information technology most
advancing science
•Concurrent advances in information technology
infrastructure and mobile computing power in
many low and middle-income countries (LMICs)
have raised hopes that artificial intelligence (AI)
might help to address challenges unique to the
field of global health and accelerate
achievement of the health-related sustainable
development goals

3. Beginning of Artificial intelligence
• Artificial intelligence (AI) is the
term used to describe the use of
computers and technology to
simulate intelligent behavior and
critical thinking comparable to a
human being. John McCarthy
first described the term AI in
1956 as the science and
engineering of making intelligent
machines.

4. • AI promises to change the
practice of medicine in hitherto
unknown ways, but many of its
practical applications are still in
their infancy and need to be
explored and developed better.
Medical professionals also need
to understand and acclimatize
themselves with these advances
for better healthcare delivery to
the masses.

5. • Radiology is the branch that has
been the most upfront and
welcoming to the use of new
technology.[6] Computers being
initially used in clinical imaging
for administrative work like
image acquisition and storage to
now becoming an indispensable
component of the work
environment with the origin of
picture archiving and
communication system.

6. Why the developing and resource free
countries inneed of Artificial Intelligence
• These challenges, including acute
health workforce shortages and
weak public health surveillance
systems, undermine global
progress towards achieving the
health-related sustainable
development goals (SDGs).
Although not unique to such
countries, these challenges are
particularly relevant given their
contribution to morbidity and
mortality.

7. Accelerating access to A
• Numerous data are
available to show how AI is
being tested to address
health challenges relevant
to the achievement of
SDGs. Such interventions
include disease specific
applications and those
aimed at strengthening
health systems.

8. • Many AI health interventions have shown promising
preliminary results, and could soon be used to augment
existing strategies for delivering health services in LMICs.
Especially in disease diagnosis, where AIpowered
interventions could be used in countries with insufficient
numbers of health providers, and in risk
assessmentwhere tools based largely on machine learning
could help to supplement clinical knowledge. Although
the research identified in this Revie

9. Clinical use is increasing
• Healthcare providers are responsible for ensuring that AI applications
provide useful technology in support of patient care. For this reason,
gaining adequate knowledge and skills regarding AI applications in
medicine is crucial for medical students, who may even have to use
applications that did not exist during their education.

10. Benefits of AI in medicine
• Informed patient care
• ntegrating medical AI into clinician workflows can give providers
valuable context while they're making care decisions. A trained
machine learning algorithm can help cut down on research time by
giving clinicians valuable search results with evidence-based insights
about treatments and procedures while the patient is still in the room
with them.

11. Error reduction
• There is some evidence that AI can help improve patient safety. A
recent systemic review of 53 peer-reviewed studies examining the
impact of AI on patient safety found that AI-powered decision
support tools can help improve error detection and drug
management.

12. Reducing the costs of care
• There are a lot of potential ways AI could reduce costs across the
healthcare industry. Some of the most promising opportunities
include reducing medication errors, customized virtual health
assistance, fraud prevention, and supporting more efficient
administrative and clinical workflows.

13. Increasing doctor-patient engagement
• Many patients think of questions outside of typical business hours. AI
can help provide around-the-clock support through chatbots that can
answer basic questions and give patients resources when their
provider’s office isn’t open. AI could also potentially be used to triage
questions and flag information for further review, which could help
alert providers to health changes that need additional attention.

14. Providing contextual relevance
• One major advantage of deep learning is that AI algorithms can use
context to distinguish between different types of information. For
example, if a clinical note includes a list of a patient's current
medications along with a new medication their provider
recommends, a well-trained AI algorithm can use natural language
processing to identify which medications belong in the patient's
medical history.

15. • Understanding how today’s medical student perceives AI in medicine,
what they know and don’t know, and their comprehension of AI’s
ethical dimensions is a crucial first step to developing effective AI
curricula. Kern et al. define one of the steps of curriculum
development as “Needs Assessment of Targeted Learners”, a process
by which curriculum developers identify the differences between the
ideal and the actual characteristics of the targeted learner group [12].

16. • he findings conclude that future physicians are usually not familiar
with AI, their concern of losing their jobs is considerable, they are
enthusiastic to learn and use AI in their practice, and they think AI
applications in medicine should be integrated into the curriculum.

17. • In addition, Bisdas et al. concluded that there might be a high
demand to have AI topics integrated into the university curricula
which should be further explored [16]. To the best of our knowledge,
Wood et al.’s study with 117 medical students on seven topics is the
only one in the literature that investigated the importance of AI topics
in the eyes of the students. Hence, in this study, we aimed to examine
medical students’ perceptions regarding the possible influence of AI
on medicine and also their thoughts on the AI topics to be integrated
into the medical curriculum.

18. AI applications in medicine
• There are numerous ways AI can positively impact the practice of
medicine, whether it's through speeding up the pace of research or
helping clinicians make better decisions. Here are some examples of
how AI could be used:

19. AI in disease detection and diagnosis
• Unlike humans, AI never needs to sleep. Machine learning models
could be used to observe the vital signs of patients receiving critical
care and alert clinicians if certain risk factors increase. While medical
devices like heart monitors can track vital signs, AI can collect the
data from those devices and look for more complex conditions, such
as sepsis. One IBM client has developed a predictive AI model for
premature babies that is 75% accurate in detecting severe sepsis.

20. Personalized disease treatment
• Precision medicine could become easier to support with virtual AI
assistance. Because AI models can learn and retain preferences, AI
has the potential to provide customized real-time recommendations
to patients around the clock. Rather than having to repeat
information with a new person each time, a healthcare system could
offer patients around-the-clock access to an AI-powered virtual
assistant that could answer questions based on the patient's medical
history, preferences and personal needs.

21. AI in medical imaging
• AI is already playing a prominent role in medical imaging. Research
has indicated that AI powered by artificial neural networks can be just
as effective as human radiologists at detecting signs of breast cancer
as well as other conditions. In addition to helping clinicians spot early
signs of disease, AI can also help make the staggering number of
medical images that clinicians have to keep track of more manageable
by detecting vital pieces of a patient's history and presenting the
relevant images to them.

22. Clinical trial efficiency
• A lot of time is spent during clinical trials assigning medical codes to
patient outcomes and updating the relevant datasets. AI can help
speed this process up by providing a quicker and more intelligent
search for medical codes. Two IBM Watson Health clients recently
found that with AI, they could reduce their number of medical code
searches by more than 70%.

23. Accelerated drug development
• Drug discovery is often one of the longest and most costly parts of
drug development. AI could help reduce the costs of developing new
medicines in primarily two ways: creating better drug designs and
finding promising new drug combinations. With AI, many of the big
data challenges facing the life sciences industry could be overcome.

24. References
• Overview of artificial intelligence in medicine
• Amisha,1 Paras Malik,1 Monika Pathania,1 and Vyas Kumar Rathaur2
• Author information Article notes Copyright and License information
• What is artificial intelligence in medicine? IBM