Artificial intelligence shows promise in helping to control infectious diseases and reduce antimicrobial resistance in three key ways:
1) AI can enhance disease surveillance and early detection of outbreaks by integrating diverse data sources to identify patterns.
2) It can help optimize antimicrobial treatment by recommending personalized therapy regimens based on a patient's clinical information.
3) Over time, AI may become an indispensable public health tool by facilitating more accurate intervention strategies and optimizing resource allocation to curb disease spread.
Please share this webinar with anyone who may be interested!
Watch all our webinars: https://www.youtube.com/playlist?list=PL4dDQscmFYu_ezxuxnAE61hx4JlqAKXpR
Cancer care is increasingly tailored to individual patients, who can undergo genetic or biomarker testing soon after diagnosis, to determine which treatments have the best chance of shrinking or eliminating tumours.
In this webinar, a pathologist and clinical oncologist discuss:
● how they are using these new tests,
● how they communicate results and treatment options to patients and caregivers, and
● how patients can be better informed on the kinds of tests that are in development or in use across Canada
View the video: https://youtu.be/_Wai_uMQKEQ
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Precision Medicine: Four Trends Make It PossibleHealth Catalyst
When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.
Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:
Decision support methods harness the power of the human genome.
Healthcare leverages big data analytics and machine learning.
Reimbursement methods incentivize health systems to keep patients well.
Emerging tools enable more data, more interoperability.
Clinical Genomics for Personalized Cancer Medicine: Recent Advances, Challeng...Yoon Sup Choi
I reviewed recent advances, challenges, and opportunities to implement clinical cancer genomics. Case studies of advanced systems, such as Foundation Medicine, MI-ONCOSEQ are introduced for benchmark. A few fundamental limitations to establish personalized oncology are also discussed.
Cancer Immunotherapies (Focus on Melanoma & Lung Cancers)Zeena Nackerdien
Effective immunotherapy i.e. enlisting the patient’s own immune system to fight disease may mark a milestone in the fight against certain cancers. Three lymphocytes – T cells, B cells and NK-cells – involved in specific immune responses against cancers and other diseases. T cells recognize specific antigens via a T-cell antigen-receptor. The two main types of T cells, CD4- and CD8 T-cells, are categorized according to their respective CD4 and CD8 surface markers. The latter group includes cytotoxic T cells, also known as killer T lymphocytes. These cells kill invading pathogens or other disease-causing agents. Scientists discovered that a type of protein receptor, cytotoxic T-Lymphocyte Antigen 4 (CTLA-4), prevented T cells from launching immune attacks [1]. In the early 1990s, another “brake” was discovered in dying T cells namely programmed death 1 or PD-1. The rationale underlying cancer immunotherapy is that exposing CTLA-4, PD-1 or using other appropriate immune-system-based therapies may enable the activation of the immune system to destroy cancer.
Genetically engineering a patient’s T cells to target tumor cells marked one of the promising turning points in cancer immunotherapy, particularly for certain blood cancers and solid tumors. Melanoma and lung cancer, two often-fatal diseases, are treatable in the early stages with surgery or other standards of care. However, some patients are diagnosed during the later stages of the disease or relapse with refractory/unresectable tumors. For these subgroups, the latest National Comprehensive Cancer Network (NCCN) tailored algorithms coupled with systemic treatment options, including immunotherapies, could potentially improve outcomes. Here, I summarize the latest approved immunotherapies mentioned in the NCCN guidelines, along with other examples of investigational agents such as monoclonal antibodies, cancer vaccines, and natural killer cells. Additional examples of targeted therapies, novel “druggable” and other immunotargets are presented in the section, ”Future Directions.”
Reference
1. Couzin-Frankel, J., Breakthrough of the year 2013. Cancer immunotherapy. Science, 2013. 342(6165): p. 1432-3.
this slide contain information about antibody mediated anti-cancer therapy like antibody drug conjugates (ADC), Bispecific monoclonal antibody, Immuno-checkpoint therapy, biomarkers, mechanism of action of all 3 therapies, approved drugs of each category
Machine learning approaches in the diagnosis of infectious diseases-a review.pdfSmriti Mishra
Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.
Please share this webinar with anyone who may be interested!
Watch all our webinars: https://www.youtube.com/playlist?list=PL4dDQscmFYu_ezxuxnAE61hx4JlqAKXpR
Cancer care is increasingly tailored to individual patients, who can undergo genetic or biomarker testing soon after diagnosis, to determine which treatments have the best chance of shrinking or eliminating tumours.
In this webinar, a pathologist and clinical oncologist discuss:
● how they are using these new tests,
● how they communicate results and treatment options to patients and caregivers, and
● how patients can be better informed on the kinds of tests that are in development or in use across Canada
View the video: https://youtu.be/_Wai_uMQKEQ
Follow our social media accounts:
Twitter - https://twitter.com/survivornetca
Facebook - https://www.facebook.com/CanadianSurvivorNet
Pinterest - https://www.pinterest.com/survivornetwork
YouTube - https://www.youtube.com/user/Survivornetca
Precision Medicine: Four Trends Make It PossibleHealth Catalyst
When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.
Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:
Decision support methods harness the power of the human genome.
Healthcare leverages big data analytics and machine learning.
Reimbursement methods incentivize health systems to keep patients well.
Emerging tools enable more data, more interoperability.
Clinical Genomics for Personalized Cancer Medicine: Recent Advances, Challeng...Yoon Sup Choi
I reviewed recent advances, challenges, and opportunities to implement clinical cancer genomics. Case studies of advanced systems, such as Foundation Medicine, MI-ONCOSEQ are introduced for benchmark. A few fundamental limitations to establish personalized oncology are also discussed.
Cancer Immunotherapies (Focus on Melanoma & Lung Cancers)Zeena Nackerdien
Effective immunotherapy i.e. enlisting the patient’s own immune system to fight disease may mark a milestone in the fight against certain cancers. Three lymphocytes – T cells, B cells and NK-cells – involved in specific immune responses against cancers and other diseases. T cells recognize specific antigens via a T-cell antigen-receptor. The two main types of T cells, CD4- and CD8 T-cells, are categorized according to their respective CD4 and CD8 surface markers. The latter group includes cytotoxic T cells, also known as killer T lymphocytes. These cells kill invading pathogens or other disease-causing agents. Scientists discovered that a type of protein receptor, cytotoxic T-Lymphocyte Antigen 4 (CTLA-4), prevented T cells from launching immune attacks [1]. In the early 1990s, another “brake” was discovered in dying T cells namely programmed death 1 or PD-1. The rationale underlying cancer immunotherapy is that exposing CTLA-4, PD-1 or using other appropriate immune-system-based therapies may enable the activation of the immune system to destroy cancer.
Genetically engineering a patient’s T cells to target tumor cells marked one of the promising turning points in cancer immunotherapy, particularly for certain blood cancers and solid tumors. Melanoma and lung cancer, two often-fatal diseases, are treatable in the early stages with surgery or other standards of care. However, some patients are diagnosed during the later stages of the disease or relapse with refractory/unresectable tumors. For these subgroups, the latest National Comprehensive Cancer Network (NCCN) tailored algorithms coupled with systemic treatment options, including immunotherapies, could potentially improve outcomes. Here, I summarize the latest approved immunotherapies mentioned in the NCCN guidelines, along with other examples of investigational agents such as monoclonal antibodies, cancer vaccines, and natural killer cells. Additional examples of targeted therapies, novel “druggable” and other immunotargets are presented in the section, ”Future Directions.”
Reference
1. Couzin-Frankel, J., Breakthrough of the year 2013. Cancer immunotherapy. Science, 2013. 342(6165): p. 1432-3.
this slide contain information about antibody mediated anti-cancer therapy like antibody drug conjugates (ADC), Bispecific monoclonal antibody, Immuno-checkpoint therapy, biomarkers, mechanism of action of all 3 therapies, approved drugs of each category
Machine learning approaches in the diagnosis of infectious diseases-a review.pdfSmriti Mishra
Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.
AI, Machine Learning Playing Important Role In Fighting COVID-19 OpenTeQ group
This is example of research at the Radiological Society of North America. Highlighting the point out this phenomenon- that some of the patients are falling ill and dying as some others are experiencing very mild symptoms are none at all is the most mysterious element of the disease. Mortality is one of the correlations with some of the major factors like age, gender, and other major chronic conditions. Hence, more factors can be prognostic as the young individuals have succumbed to the virus.
Presents a futuristic view based on development in health and medical data processing. the concept of and future of ePatient was discussed. The risks and limitations to digital medicine were presented.
Optimising maternal & child healthcare in India through the integrated use of...Skannd Tyagi
This paper is a literature review on the present condition of pre-natal and post-natal Maternal and Child healthcare in Rural India. This is a first step on finding the several possibilities using AI, Big Data and Telemedicine in identifying patterns and provide more structured and streamlined support to rural and semi-urban communities. Our endeavour with this research paper is to identify the pain points and attempt to find solutions using current technologies.
These are the upcoming life science trends we can expect to see more in 2019. While healthcare research in immunomodulation and gene therapy is relevant; data-sharing, purpose-driven analytics and AI is gaining more popularity within the industry. With these technological aspects in place, the research community hopes to drive for more discoveries and medical breakthroughs.
Dissertation on Computer Science: Machine Learning Algorithm to Predict Covid...PhD Assistance
Artificial intelligence and data science play a vital role in the health-care business in this era of automation. Medical practitioners may simply manage their duties and patient care since these technologies is so well-connected. Dependence on automated systems such as AI has increased in healthcare services.ML can identify illness and viral infections more precisely, allowing patients’ ailments to be identified earlier, severe phases of diseases to be avoided, and fewer people to be treated.
Read More: https://bit.ly/3HGu9NY
For Enquiry:
India: +91 91769 66446
UK: +44 7537144372
Email: info@phdassistance.com
Artificial Intelligence in PharmacovigilanceClinosolIndia
The integration of Artificial Intelligence (AI) into pharmacovigilance has emerged as a transformative force, revolutionizing the monitoring and assessment of drug safety. This article provides a comprehensive overview of the application of AI in pharmacovigilance, elucidating its impact on the identification, evaluation, and management of adverse drug reactions (ADRs). AI-driven algorithms, machine learning, and natural language processing empower automated signal detection, enabling more efficient and proactive risk assessment. The review explores the utilization of AI in mining diverse data sources, including electronic health records, social media, and scientific literature, to enhance the sensitivity and specificity of ADR detection. Additionally, the article delves into the role of AI in streamlining case processing, automating data validation, and facilitating trend analysis, thereby optimizing the pharmacovigilance workflow. Challenges, such as data quality and interpretability of AI-generated insights, are critically examined, alongside ongoing efforts to address these concerns. The regulatory landscape and the incorporation of AI technologies into pharmacovigilance guidelines are discussed, highlighting the evolving framework for ensuring patient safety. As AI continues to evolve, its synergy with traditional pharmacovigilance practices opens new avenues for enhanced surveillance and proactive risk management in the dynamic field of drug safety.
Dissertation on Computer Science: Machine Learning Algorithm to Predict Covid...PhD Assistance
Artificial intelligence and data science play a vital role in the health-care business in this era of automation. Medical practitioners may simply manage their duties and patient care since these technologies is so well-connected. Dependence on automated systems such as AI has increased in healthcare services.ML can identify illness and viral infections more precisely, allowing patients’ ailments to be identified earlier, severe phases of diseases to be avoided, and fewer people to be treated.
Read More: https://bit.ly/3HGu9NY
For Enquiry:
India: +91 91769 66446
UK: +44 7537144372
Email: info@phdassistance.com
ImageVision_ Blog_ AI in Healthcare Unlocking New Possibilities for Disease D...AppsTek Corp
Healthcare has made massive developments and advancements in recent years, particularly in clinical research, biomedical improvement, digital technology, processes, and systems.
However, it nonetheless faces several complications, together with a lack of healthcare workers at the frontlines, an increase in health disparities between nations with various income levels, and a vast quantity of health spending that has not yielded the favored health outcomes. Artificial Intelligence (AI) has emerged as an approach to deal with these challenges, using technologies such as ML – Machine Learning and DL – Deep Learning.
From disease diagnosis to personalized treatment plans, the integration of AI-powered solutions has shown its capability to change the way healthcare works. The ability to process big volumes of information rapidly and appropriately has created new possibilities for enhancing patient care, lowering prices, and enhancing efficiency in the Healthcare system.
In this blog, we will explore How AI is Transforming Healthcare and its impact on both patients and Healthcare providers. let's first delve into the reasons why Healthcare is adopting AI.
Similar to Artificial Intelligence in Controlling Infectious Diseases and Reducing Antimicrobial Resistance (20)
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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
Dr.T.V.Rao MD@Artifical intilligence 3
28-11-2023
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.
Dr.T.V.Rao MD@Artifical intilligence 4
28-11-2023
5. Where I should learn Artificial
Intelligence ?
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 5
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".
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 6
7. AI applications
in Medicine
28-11-2023
• 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
Dr.T.V.Rao MD@Artifical intilligence 7
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
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 8
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 9
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 10
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
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 11
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.
Dr.T.V.Rao MD@Artifical intilligence 12
28-11-2023
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
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 13
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.
Dr.T.V.Rao MD@Artifical intilligence 14
28-11-2023
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 15
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.
28-11-2023
Dr.T.V.Rao MD@Artifical intilligence 16
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.
Dr.T.V.Rao MD@Artifical intilligence 17
28-11-2023
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 18
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 19
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.
Dr.T.V.Rao MD@Artifical intilligence 21
28-11-2023
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,
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 22
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
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 23
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 25
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,
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 26
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.
•
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 27
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 28
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).
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 29
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
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 30
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?
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 31
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.
28-11-2023 Dr.T.V.Rao MD@Artifical intilligence 32
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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