Artificial Intelligence in Controlling Infectious Diseases and Reducing Antimicrobial Resistance

1. Artificial Intelligence in Controlling
Infectious Diseases and Reducing
Antimicrobial Resistance
Dr.T.V.Rao MD
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2. Thanks To
•Pujitha Naidu Conference Manager
Microbiology & Infectious Diseases
2023 Aver Conferences Organizers of
3rd International Conference on
Microbiology & Infectious Diseases
Dubai UAE
Copyright ©2023 All rights reserved
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3. Changing Medical profession with Artificial
Intelligence what it means to us
• Artificial Intelligence fast
penetrating to every system and
modality of human living However
the implications of Artificial
Intelligence is truly different in
human from other
professions we should be
more aware of the ongoing
matters and choose what is
good in Human and health
care ?
• Dr.T.V.Rao MD
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4. 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.
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5. Where I should learn Artificial
Intelligence ?
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6. Integrating Human Intelligence with
machines
• In computer science, artificial intelligence (AI),sometimes
called machine intelligence, is intelligence demonstrated
by machines, in contrast to the natural intelligence
displayed by humans and animals
•The term "artificial intelligence" is used to
describe machines that mimic "cognitive"
functions that humans associate with
other human minds, such as "learning"
and "problem solving".
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7. AI applications
in Medicine
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• The primary aim of health-related AI
applications is to analyze relationships
between prevention or treatment
techniques and patient
outcomes.
AI programs have been developed and
applied to practices such as diagnosis
processes, treatment protocol
development, drug
development, personalized
medicine, and patient
monitoring and care
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8. Infectious diseases
• Infectious diseases are
caused by
microorganisms belonging
to the class of bacteria, viruses,
fungi, or parasites. These
pathogens are transmitted,
directly or indirectly, and can
lead to epidemics or even
pandemics
• Recent pandemic of
Covid 19 and
associated mortality
and morbidity
shaken the world
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9. Creating algorithm,
• Create the algorithm, the
researchers used data,
including behavioural,
demographic and
epidemic disease
trends, to create a model of
disease spread that captures
underlying population
dynamics and contact
patterns between people.
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10. HOW AI HELPS IN INFECTIOUS
DISEASES
• One of the ways that AI can help to prevent infectious
diseases is by enhancing the surveillance and
identification of emerging
infectionsand their potential sources,
vectors, and transmission routes. AI can
integrate large amounts of data from different sources, such
as genomic, epidemiological, environmental, and social data,
and use machine learning algorithms to find patterns, trends,
and anomalies that can indicate the presence or risk of an
outbreak.
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11. AI can help to treat infectious
diseases
AI can help to treat infectious diseases is by enhancing the
selection and optimization of antimicrobial therapy
and infection prevention and control measures.
AI can use machine learning to analyse data from
clinical trials, electronic health records, and
pharmacokinetic and pharmacodynamic
models, and provide personalized and evidence-based
recommendations for the best antimicrobial regimen, dosage,
duration, and route of administration for each patient
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12. Google helps in use of Artificial
intelligence
• well-known software
program Verily, a
Google product, is
adept at predicting
both
communicable
and hereditary
genetic disorders.
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13. Artificial intelligence is everywhere
AI is at your Reach
• Artificial intelligence is
everywhere, influencing
every branch of science and
humanities and now a new
addition to the infectious
disease researchers
modality to growing problem
of Infectious diseases
• Google continues to be
master of Artificial intilligence
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14. Clinical use of AI 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.
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15. The Real Time happenings in
Infectious diseases
• integration of AI in clinical
microbiology for image analysis
of gram stains, automated
digital culture plate reading,
identification of bacterial
isolates using matrix-assisted
laser desorption-
ionization/time of flight mass
spectrometry (MALDI-TOF)
data, and whole genome
sequencing of microbial
pathogens.
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16. Public health concerns on
infectious diseases can be
supported by artificial
intelligence
• Th challenges, along with rising care costs
associated with infectious disease, are met with the
concurrent emergence of artificial intelligence in
health care. Through health care informatics, AI-
based machine learning
techniques are gaining
prominence in public health
planning, by translating large, heterogeneous,
and often disparate datasets into effective public
health management tools.
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17. What the The Medical Futurist says
• At The Medical Futurist, we are
sure that Artificial
Intelligence is not going to
replace medical
professionals; it’s going to
be the stethoscope of the
21st century and successful
collaboration between humans
and technology could bring us
the positive change in medicine
that we all so strongly wish.
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18. Tracking the Infectious diseases
Greater use in Pandemics
• Artificial intelligence track
the infection outbreaks
faster, Aimed to assist
public health officials
identify and treat
individuals with
undiagnosed infectious
diseases, the algorithm was
created with the hope it
could reduce how quickly
disease spreads.
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19. Information ML tools In Infectious
diseases
• We found 60 unique ML-clinical decision support systems
(ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%)
focused on bacterial infections, 10 (17%) on viral infections,
nine (15%) on tuberculosis and four (7%) on any kind of
infection. Among them, 20 (33%) addressed the diagnosis
of infection, 18 (30%) the prediction, early detection or
stratification of sepsis, 13 (22%) the prediction of treatment
response, four (7%) the prediction of antibiotic resistance,
three (5%) the choice of antibiotic regimen and two (3%)
the choice of a combination antiretroviral therapy.
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21. Future of Artificial Intelligence in
Medicine
• Artificial intelligence moved from
being a futuristic promise into a
reference point for innovation. The
technology also started to
transform medicine with great
vigor. In the last couple of
years, the number of A.I.-
related studies, research
projects, university courses,
and companies has grown
exponentially, not to speak about
the rapid improvement in the
precision of the technology.
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22. Artificial intelligence reduces costs and
optimize the detection and control of
infectious diseases
•Considering the ballooning
costs of care, the continued
emergence of new infectious
diseases, and the rise of
antimicrobial resistance,
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23. Indispensable tool for public health
policy planning
•AI will likely become an indispensable tool for
public health policy planning. In the future,
algorithms will facilitate more accurate and
efficient intervention and prevention
strategies, optimize the allocation of scarce
resources, and ultimately curtail the spread of
infectious disease
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25. Artificial intelligence
helps in monitoring
infectious diseases
• This capability has the potential
to produce effective tools to assist
in infectious-disease prevention
and the discovery of cures. If used
effectively and judiciously in the
coming months and years, AI’s
unparalleled ability to
maximize and create new
opportunities to detect and
monitor infectious diseases will
be realized.
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26. The Lancet Endorses the Importance
of Internet based surveillance
• A study published in 2013 in
the journal The Lancet
confirmed that Internet-based
surveillance can detect
outbreaks of infectious
diseases such as influenza and
dengue fever one to two
weeks sooner than traditional
reporting methods,
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27. Use Misuse of Antibiotics
Widespread use of
antibiotics in clinical
practice has not only
resulted in drug
resistance but has also
increased the threat of
super-resistant
bacteria emergence.
•
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28. Designing New drugs and Antibiotics
• AI has been applied to
design new antibiotics,
and generates a synergy of a
combination of drugs The
machine-learning algorithms
analyse patterns in data on
antimicrobial use and
resistance to predict which
micro-organisms are likely to
develop resistance to certain
drugs.
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29. Artificial Intelligence in Detection of
Antibiotic resstiance
• Antibiotic resistance (AMR) is, unfortunately, a result
of antibiotic misuse. As AMR drastically reduces
antibiotic therapeutic efficacy, it is critical that we
follow its emergence and dissemination . Currently,
two approaches for diagnosing AMR are commonly
utilized. One is called the whole-genome
sequencing for antimicrobial susceptibility
testing (WGS-AST) and the other one is antibiotic
susceptibility testing (AST).
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30. Great questions and emerging
challenges
• How to develop artificial
Intelligence at our Institutions
• How to train to Medical
Nursing and Paramedical
professionals
• Costs of Infrastructure who
will bear it
• How to regulate the Matters
•Legal Implication in
treating the patients
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31. Where We stand in Use of Artificial
Intelligence ???
• Everyone should study and learn
about these areas related to
artificial intelligence:
• What is artificial intelligence?
• How could I apply it in my
area of interest?
• How will it change our short
term and long-term future?
• What are the challenges and
opportunities presented by
AI?
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32. Future of Artificial intelligence
?
•The rise of powerful
AI will be either the
best, or the worst
thing, ever to happen
to humanity. We do
not yet know which,"
said Stephen
Hawking.
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33. References
• Use of artificial intelligence in infectious diseases -Said Agrebi a , Anis
Larbi b,ca Yobitrust, Technopark El Gazala, Ariana, Tunisia, b
• Singapore Immunology Network, Agencyfor Science, Technology and
Research, Singapore, Singapore, cDepartment of Microbiology &
Immunology, Yong Loo Lin School of Medicine, National University of
Singapore,Singapore,
• Machine learning for clinical decision support in infectious diseases:
narrative review of current applicationsN. Peiffer-Smadja 1, 2, *, T.M.
Rawson 1, R. Ahmad 1, A. Buchard 3, P. Georgiou 4,F.-X. Lescure 2, 5
G. Birgand 1 A.H. Holmes 1
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