Here is a proposed rubric to assess answers to the question "What are the antibiotics for leprosy treatment?":
4 - Identifies both rifampicin and streptomycin as first-line antibiotics for leprosy treatment. May also mention dapsone as an alternative for resistant cases. Shows understanding that rifampicin is the primary antibiotic.
3 - Identifies both rifampicin and streptomycin but does not provide context about them being first-line. May be missing detail about dapsone. Answer is largely correct but lacks some context.
2 - Identifies one of the main antibiotics (rifampicin or streptomycin) but is missing the other. May provide an incorrect or irrelevant
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more.
Artificial Intelligence In Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
How can we make a Radiologist more efficient?
Increased Imaging for Chronic Diseases and Emergencies raise the demand for radiologists globally & AI could definitely assist them in increasing their efficiency & meet the requirements.
A one day workshop on the use of AI in Healthcare for practice, teaching and research.
The Resource Material for the "AI in Healthcare" workshop serves as an essential guide for healthcare professionals who aim to harness the transformative power of Artificial Intelligence (AI) in clinical practice, medical education, and research. Developed under the expertise of Dr Vaikunthan Rajaratnam, this comprehensive package is designed to complement the workshop, providing both foundational knowledge and practical tools for immediate application.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
From traffic routing to self-driving cars, Alexa to Siri, AI’s reach is extending into all areas of life, including healthcare. Join Kimberley to learn more about how AI is being used now, and will be used in the near future, to facilitate provider-patient communication, mine medical records, assess patients, predict illness, suggest treatments, and so much more.
Artificial Intelligence In Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
How can we make a Radiologist more efficient?
Increased Imaging for Chronic Diseases and Emergencies raise the demand for radiologists globally & AI could definitely assist them in increasing their efficiency & meet the requirements.
A one day workshop on the use of AI in Healthcare for practice, teaching and research.
The Resource Material for the "AI in Healthcare" workshop serves as an essential guide for healthcare professionals who aim to harness the transformative power of Artificial Intelligence (AI) in clinical practice, medical education, and research. Developed under the expertise of Dr Vaikunthan Rajaratnam, this comprehensive package is designed to complement the workshop, providing both foundational knowledge and practical tools for immediate application.
The integration of data analytics in healthcare contributes to more informed decision-making, better patient outcomes, and increased efficiency throughout the healthcare ecosystem. It also paves the way for ongoing advancements in the field of medical research and healthcare delivery.
Theory and Practice of Integrating Machine Learning and Conventional Statisti...University of Malaya
The practice of medical decision making is changing rapidly with the development of innovative
computing technologies. The growing interest of data analysis in line with the advancement in data
science raises the question of whether machine learning can be integrated with conventional statistics
in health research. To help address this knowledge gap, this talk focuses on the conceptual
integration between conventional statistics and machine learning, with a direction towards health
research. The similarities and differences between the two are compared using mathematical
concepts and algorithms. The comparison between conventional statistics and machine learning
methods indicates that conventional statistics are the fundamental basis of machine learning, where
the black box algorithms are derived from basic mathematics, but are advanced in terms of
automated analysis, handling big data and providing interactive visualizations. While the nature of
both these methods are different, they are conceptually similar. The evidence produced here
concludes that conventional statistics and machine learning are best to be integrated to develop
automated data analysis tools. Health researchers may explore machine learning as a potential tool to
enhance conventional statistics in data analytics for added reliable validation measures.
Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Business Analytics in healthcare industry.pptxGauravMalve2
Hey there!
Exciting news – we're diving into the fascinating world of Business Analytics in the Healthcare sector, and I've just uploaded a killer PowerPoint presentation on SlideShare that you won't want to miss!
🏥 Title: Unveiling the Power of Business Analytics in Healthcare
🚀 Description:
Hey, fellow data enthusiasts! 👋 Get ready to embark on a journey through the dynamic realm where business analytics meets healthcare. Our latest presentation explores the impactful synergy between data-driven insights and the healthcare sector's ever-evolving landscape.
👉 Key Highlights:
Uncovering the role of analytics in optimizing healthcare operations.
Real-world examples showcasing improved patient outcomes through data analysis.
Navigating the challenges and opportunities in healthcare analytics.
Future trends that promise to reshape the healthcare analytics landscape.
🌐 SlideShare Link: Business Analytics in Healthcare
👀 Why You Should Check it Out:
Whether you're a healthcare professional, data enthusiast, or just someone intrigued by the magic that happens when numbers meet healthcare, this presentation is tailor-made for you! Gain insights, spark discussions, and stay ahead of the curve in understanding how analytics is revolutionizing the healthcare game.
Ready to elevate your understanding of business analytics in healthcare? Click the link above and let the learning begin! 🚀
Feel free to share with your network and dive into the discussion. Let's amplify the conversation around data-driven healthcare together!
Cheers !!!
Leveraging Data Analysis for Advancements in Healthcare and Medical Research.pdfSoumodeep Nanee Kundu
Data analysis in healthcare encompasses a wide range of applications, all geared toward improving patient care and well-being. It begins with the collection of diverse healthcare data, which includes electronic health records, medical imaging, genomic data, wearable device data, and more. These data sources provide a rich tapestry of information that can be analysed to unlock valuable insights and drive healthcare advancements.
One of the primary areas where data analysis is a game-changer is in clinical decision-making. Through the utilization of data-driven algorithms, healthcare professionals are empowered to make informed decisions regarding patient diagnosis, treatment plans, and prognosis. Clinical Decision Support Systems (CDSS), powered by data analysis, provide real-time guidance based on evidence-based medical knowledge, assisting physicians in choosing the most appropriate treatments and interventions. This not only enhances patient care but also reduces medical errors and ensures that treatment decisions are aligned with the most current medical research.
Data analysis is also instrumental in early disease identification and monitoring. Machine learning models, for example, can predict the onset of diseases like diabetes, Alzheimer's, and cardiovascular conditions by analysing patient data. This early detection capability enables healthcare providers to intervene proactively, potentially preventing or mitigating the severity of these conditions. This aspect of data analysis significantly contributes to the shift from reactive to proactive healthcare, improving patient outcomes and reducing healthcare costs.
Epidemiology and public health are areas where data analysis plays a vital role. The analysis of healthcare data is essential for tracking and predicting disease outbreaks, which is especially critical in the context of infectious diseases and bioterrorism preparedness. Real-time analysis of health data can offer early warning signs of emerging epidemics, allowing authorities to take timely preventive measures and allocate resources efficiently.
Changing Medical profession with Artifical Intelligence what it means to us Dr.T.V.Rao MD
•Artificial Intelligence fast penetrating to every system and modality of human living However the implications of Artificial Intelligence is truly different from other professions we should be more aware of the ongoing matters and chose what is good in Human and health care ?
•Dr.T.V.Rao MD
•Former professor of Microbiology
•Adviser and Member Associate Elsevier research Netherlands
> Definition of RWD
> RWD - Big Data Characteristics
> Sources of RWD
> Important Stakeholders
> Benefits of RWD
> Why Data Sharing is Important?
> Benefits of Data Sharing
> Who Benefits?
> Ultimate Goals
> Case Studies
> Challenges
> Data Privacy Scenario
> Data Security in India
> Regulatory Perspectives Around RWD
> How to Encourage Data Sharing?
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
The Power of Data Analytics in Smart HealthcareWerkDone
Data analytics involves the use of various techniques to analyze and interpret large amounts of data to uncover patterns and insights. In healthcare, data analytics can be used improve the delivery of patient care, predict disease outbreaks, and develop personalized treatment plans.
Precision and Participatory Medicine - Medinfo 2015 Panel on big data. Includes the proposal to use the term Expotype to characterise the Exposome of an individual. Electronic expo typing would refer to the automatic construction of individual expo types from electronic clinical records and other sources of environmental risk factor and exposure data.
Combining Patient Records, Genomic Data and Environmental Data to Enable Tran...Perficient, Inc.
The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
AI in Healthcare APU Using AI in Healthcare for clinical Application research...Vaikunthan Rajaratnam
Discover how generative AI is transforming the face of healthcare. From accelerating drug discovery to empowering personalized treatment, this technology is reshaping the way we deliver and experience care."
Generative AI in Health Care a scoping review and a persoanl experience.Vaikunthan Rajaratnam
A scoping review of the literature, its impact and challenges in healthcare, and a personal experience of its application in practice, teaching, and research.
COMPARATIVE ANALYSIS OF CHATGPT-4 AND CO-PILOT IN CLINICAL EDUCATION: INSIGHT...Vaikunthan Rajaratnam
This research investigates the potential of two advanced AI language models, ChatGPT-4 and Co-Pilot, to transform medical education through clinical scenario generation. Focusing on scenarios for Diabetic Neuropathy, Acute Myocardial Infarction, and Pediatric Asthma, the study compares the accuracy, depth, and practical teaching utility of content generated by each platform. A panel of medical experts assessed the AI-generated scenarios, and healthcare professionals provided feedback on their perceived usefulness in educational settings. Results suggest that ChatGPT-4 excels in providing structured foundational knowledge, while Co-Pilot offers greater depth through realistic patient narratives and a focus on holistic care. This indicates that both platforms have value, with their suitability depending on specific educational objectives – ChatGPT-4 aligns better with introductory learning, and Co-Pilot better serves advanced applications emphasizing practical clinical reasoning.
This workshop is a comprehensive introduction to the application of Generative AI in healthcare. It provides healthcare professionals, educators, and researchers with practical experience in using Generative AI for data analysis, predictive modeling, and personalized treatment planning. The workshop also explores the use of Generative AI in medical education and research. No prior AI experience is required, making this a unique opportunity to learn about the latest advancements in Generative AI and its healthcare applications.
This workshop will empower healthcare professionals with the knowledge and skills to leverage artificial intelligence (AI) in their practice. It aims to bridge the gap between cutting-edge technology and everyday clinical, research, and educational practice. The platforms covered in the workshop include Elicit.org, Scholarcy.com, Typeset.io, ChatGPT, Botpress.com, InVideo.io, and Genie.io.
The objectives of this specialised workshop are to:
• Explore the core principles of AI, emphasising its applications and significance in modern healthcare.
• Examine the role of AI in enhancing clinical judgment and patient management, with live demonstrations of relevant tools.
• Uncover the potential of AI in revolutionising teaching and learning experiences for healthcare professionals and students.
• Illustrate the integration of AI in healthcare research, focusing on tasks such as literature review, data analytics, and manuscript development.
• Provide a hands-on experience with various AI platforms tailored to healthcare professionals' unique needs and demands
The slide deck for the "AI for Learning Design" workshop, hosted at Asia Pacific University, serves as a comprehensive guide to integrating Artificial Intelligence into educational settings. Designed to empower educators and instructional designers, the presentation offers actionable strategies for curriculum integration, insights into personalized learning through AI, and a deep dive into the ethical considerations that accompany AI adoption in education. The deck is structured to facilitate an interactive and engaging workshop experience, featuring real-world examples, hands-on activities, and spaces for thought-provoking discussions. Don't miss this invaluable resource for transforming your teaching practices and enhancing educational impact through AI.
empowereing practice in healthcare with generative AI. How to use vairous AI tools to enhance and empowere healthc are practice inlcuidng teaching and research
Academic writing is the backbone of scholarly communication and is vital in knowledge dissemination. However, it can often be challenging and time-consuming, requiring meticulous attention to detail and adherence to established conventions. This is where AI comes into play, offering innovative solutions to streamline and enhance the writing process.
Strategies to reduce post op pain in amputation. Candidates for limb amputation
Risk of developing post-operative pain and phantom limb pain.
Willing and able to participate in post-operative rehabilitation and physical therapy.
Informed consent for the procedure and understand the potential risks and benefits.
Adequate muscle function to allow for TMR surgery to be performed.
Suitable for TMR surgery as per a surgeon's assessment.
Validated tools for assessment of medical disability
At the end of this lecture you will be able to:-
Describe the impact of disease/injury on an individual
List the requirements of an instrument to measure disability
Describe the features of the WHOIDAS 2.0 instrument and its role in medical disability assessment
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stockrebeccabio
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MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...
AI in Practice for Healthcare
1. From Scalpel to
Algorithm
How AI is Revolutionizing
Medical Education,
Research and Clinical
Practice
Vaikunthan Rajaratnam
Hand Surgeon, Medical Educator and
Instructional Designer
2. Disclaimer
I am not an AI expert, nor do I
possess coding knowledge
specific to the underlying
mechanisms of AI models; my
expertise lies in the utilisation
of these models, such as
ChatGPT, based on my
extensive experience as a user
within the fields of healthcare,
medical education, and related
research, rather than their
technical development or
underlying algorithms.
3. Introduction to AI in
Healthcare:
Opportunities and
Challenges
AI technologies have the potential to
revolutionize healthcare by enhancing
diagnosis, treatment planning, and research.
AI won't replace you, but someone
empowered by AI undoubtedly will
4. Understanding AI, Generative AI, and ChatGPT
• AI (Artificial Intelligence)
• refers to the simulation of human intelligence in
machines that are programmed to think, learn, and
make decisions
• Applications: Includes machine learning, natural
language processing, robotics, computer vision, etc.
• Generative AI
• subset of AI that focuses on creating new data
instances that are similar to a set of training
examples.
• Techniques: Examples include Generative Adversarial
Networks (GANs), Variational Autoencoders (VAEs),
etc.
• ChatGPT (Generative Pretrained Transformer):
• State-of-the-art language models developed by
OpenAI. It utilises the Transformer architecture to
generate human-like text based on given prompts.
• Usage: Widely used in natural language understanding
tasks, chatbots, content creation, and more.
5. Suero-Abreu, G. A., Hamid, A., Akbilgic, O., &
Brown, S.-A. (2022). Trends in cardiology and
oncology artificial intelligence publications.
American Heart Journal Plus: Cardiology
Research and Practice, 17, 100162.
https://doi.org/10.1016/j.ahjo.2022.100162
6. • Rapid multi-disciplinary
stream of authors
researching AI in Medicine
• Skills and data quality
awareness for data-
intensive analysis
• Limitations
• Ethics,
• Data governance, and
• Competencies of the health
workforce.
• Focuses on
• Health services
management
• Predictive medicine
• Patient data and diagnostics
• Clinical decision-making
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured
literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9
7. Health
services
managemen
t
• Optimization of Operational Efficiency
• Example: Scheduling algorithms to optimize staff shifts and patient appointments, reducing wait times.
• Predictive Analytics for Resource Allocation
• Example: Predicting hospital bed occupancy based on patient flow and admission trends for better
resource planning.
• Supply Chain Optimization
• Example: Forecasting the need for medical supplies and automating procurement to reduce inventory
costs.
• Fraud Detection and Compliance
• Example: Detecting fraudulent billing activities and ensuring compliance with healthcare regulations.
• Integration of Care across Providers
• Example: Facilitating seamless information sharing among healthcare providers for coordinated care.
• Enhancing Administrative Decision-Making
• Example: Utilizing data analytics to inform strategic decisions, such as facility expansion or service
prioritization.
• Patient Engagement and Communication
• Example: AI-powered chatbots to handle routine inquiries, appointment scheduling, and patient follow-
ups.
• Workforce Development and Training
• Example: Using AI to identify training needs and deliver personalized learning paths for healthcare staff.
• Performance Monitoring and Quality Assurance
• Example: Implementing AI-driven analytics to monitor performance metrics, identify areas for
improvement, and ensure quality standards.
• Cost Control and Optimization
• Example: Applying AI to analyze cost drivers, identify inefficiencies, and recommend cost-saving
measures.
8. Predictiv
e
medicine
• Early Disease Detection
• Example: Using AI algorithms to analyze medical imaging for early detection of
cancers, even before symptoms appear.
• Risk Stratification
• Example: Identifying patients at high risk of chronic conditions like heart disease
based on a combination of genetic, lifestyle, and clinical data.
• Personalized Treatment Plans
• Example: Creating tailored treatment regimens by predicting individual responses
to specific drugs or therapies.
• Epidemic Outbreak Prediction
• Example: Analyzing social media, travel patterns, and other data sources to
predict the spread of infectious diseases like flu or COVID-19.
• Hospital Readmission Prediction
• Example: Determining the likelihood of a patient's readmission to the hospital,
allowing for targeted interventions to reduce readmissions.
• Drug Response Prediction
• Example: Predicting how individual patients will respond to certain medications,
minimizing adverse effects, and improving treatment efficacy.
• Genomic Medicine and Genetic Risk Prediction
• Example: Analyzing genetic data to predict susceptibility to genetic disorders and
guide preventive measures.
• Mental Health Outcome Prediction
• Example: Utilizing AI to predict mental health crises or progression of conditions
like depression based on patient behavior and medical history.
• Chronic Disease Management
• Example: Continuous monitoring and prediction of disease progression in chronic
conditions like diabetes, allowing for timely interventions.
9. Patient data
and
diagnostics
• Automated Data Analysis and Interpretation
• Example: Using AI to analyze complex laboratory results, such as genetic sequencing, to identify patterns and
anomalies.
• Real-Time Monitoring and Alerting
• Example: Continuously tracking vital signs and alerting medical staff to potential issues, such as deterioration in a
patient's condition.
• Enhanced Medical Imaging Interpretation
• Example: Applying AI algorithms to interpret radiological images, such as X-rays and MRIs, with increased accuracy
and speed.
• Predictive Analytics for Personalized Care
• Example: Analyzing patient data to predict individual responses to treatments, enabling more personalized and
effective care plans.
• Data Integration and Holistic Patient Views
• Example: Aggregating data from various sources (e.g., EMRs, wearables) to provide a comprehensive view of a
patient's health status.
• Telemedicine and Remote Diagnostics
• Example: Utilizing AI-powered tools to diagnose and manage patients in remote locations, increasing healthcare
accessibility.
• Natural Language Processing for Clinical Notes
• Example: Extracting valuable information from unstructured clinical notes through AI, enhancing data usability.
• Genomic and Precision Medicine
• Example: Integrating genomic data with clinical information to provide precise diagnoses and personalized treatment
recommendations.
• Chronic Condition Management and Monitoring
• Example: Using AI to diagnose and monitor chronic conditions, such as diabetes, through continuous data analysis.
• Ethical and Security Considerations in Data Handling
• Example: Implementing AI-driven security protocols to ensure patient data privacy and compliance with
regulations.
10. Clinical
decision-
making
• Evidence-Based Recommendations
• Example: AI systems can analyze vast medical literature to
provide evidence-based treatment recommendations tailored to
individual patient profiles.
• Diagnostic Support Tools
• Example: AI algorithms can assist physicians in diagnosing
complex conditions by analyzing clinical data, medical imaging,
and laboratory results.
• Predicting Patient Outcomes
• Example: Using AI to predict patient responses to various
treatments, aiding in selecting the most effective therapy.
• Treatment Pathway Optimization
• Example: AI can suggest optimal treatment pathways based on
patient characteristics, medical history, and current clinical
guidelines.
• Enhancing Multidisciplinary Collaboration
• Example: AI-driven platforms can facilitate collaboration among
specialists, integrating insights from various disciplines for
comprehensive care.
• Ethical Considerations in Decision Making
• Example: Implementing AI algorithms that consider ethical
principles, such as fairness and transparency, in clinical
11. Challenges
• Data
• Trust
• Ethics
• Readiness for change,
• Expertise
• Buy-in
• Regulatory strategy
• Scalability
• Evaluation
Golhar, S. P., & Kekapure, S. S. (2022). Artificial Intelligence in Healthcare—A Review. International Journal of Scientific
Research in Science and Technology, 9(4), 381–387. https://doi.org/10.32628/IJSRST229454
12. Governance
Model for AI
S. Reddy, S. Allan, S. Coghlan, and P. Cooper, ‘A governance model for the application of AI in health care’, J. Am. Med. Inform. Assoc., vol. 27, no.
3, pp. 491–497, Mar. 2020, doi: 10.1093/jamia/ocz192
Rahman, N., Thamotharampillai, T., & Rajaratnam, V. (2023). Ethics, guidelines, and policy for technology in healthcare. In
Medical Equipment Engineering: Design, Manufacture and Applications (pp. 119–147). IET Digital Library.
https://doi.org/10.1049/PBHE054E_ch9
13. Higgins, D., & Madai, V. I. (2020). From Bit to
Bedside: A Practical Framework for Artificial
Intelligence Product Development in
Healthcare. Advanced Intelligent Systems,
2(10), 2000052.
https://doi.org/10.1002/aisy.202000052
14. What is ChatGPT?
• Understanding Language
• Reads and comprehends human-written text.
• Generating Text
• Writes human-like text, from answers to creative content.
• Conversation
• Capable of engaging in text-based conversations with users.
• Applications
• Used in virtual assistants, education, content creation, and more.
• Not a Human
• Generates text through algorithms, without feelings or
consciousness.
AI for Clinical Decision-Making and Patient Care
15. How Does
ChatGPT Work?
“Don’t cry ………..”
“ Don’t cry over….”
• Reading Text:
• Takes in words, questions, or sentences as input.
• Understands the language like a human reading a book.
• Processing Information:
• Breaks down the input into smaller parts to understand the meaning.
• Uses a complex mathematical model to analyse the text.
• Generating Response:
• Constructs a response based on what it has "learned" from reading lots of text.
• Tries to make the response sound like something a human would say.
• No Personal Knowledge or Opinions:
• Doesn't have thoughts, feelings, or personal experiences.
• Answers are based on patterns in the data it was trained on, not personal beliefs
opinions.
• Learning from Data:
• Trained on a vast amount of text from books, websites, and other written materia
• Learns the structure of language and how to create sentences that make sense.
• Versatility:
• Can be used for various tasks like answering questions, writing stories, or helping
homework.
• Adaptable to different subjects and contexts.
• Not Perfect:
• Can make mistakes or provide incorrect information.
• Needs to be used with caution, especially for critical or sensitive topics
16. Understanding ChatGPT
• Advanced language
model developed by
OpenAI.
• Generates human-like
text based on the
prompts.
• Quality vs prompt.
Quality of Response ∝ Quality of Prompt × Model Understanding
Here:
Quality of Response is the measure of how relevant, accurate, and coherent the response is.
Quality of Prompt represents the clarity, specificity, and relevance of the prompt given to the model.
Model Understanding , model's ability to interpret the prompt, including its training, design, and current context.
18. Prompt Engineering
• Define the Objective:
• Identify the specific information or assistance
• Be Clear and Precise:
• Use clear language and avoid ambiguity.
• Include essential details without over-
complicating the prompt.
• Consider Context:
• Provide relevant background or context to guide
the response.
• Set the Tone and Style:
• Specify the desired tone (formal, casual) or style
(e.g., summary, explanation) if it matters for your
use case.
• Ask Direct Questions:
• If seeking specific information, formulate your
prompt as a direct question.
• Self Reflective
• Avoid Bias and Leading Questions:
• Craft the prompt neutrally to prevent biased or
skewed responses.
• Test and Refine:
• Experiment with different phrasings and observe
how slight changes can affect the response.
• Refine the prompt
• Consider Ethical and Privacy Concerns:
• Ethical guidelines and does not request or reveal
sensitive or private information.
19. Response Validation
• Review response - meets your requirements.
• No access to real-time data
• Vaildate Validate Validate.
• Prompt – response -refine - reprompt.
Relevance
Check
Accuracy
Confirmation
Context
Consistency
Sensitivity
Review
Refinement for
Future Queries
20. 67-year-old male has
dizziness every time
he sits up from a
lying position,
especially in the
morning. Also, when
he suddenly moves
his head, he notes
the dizziness.
What is the diagnosis
22. Patient Triage:
•Appropriate level of
care
Mental Health
Support:
•Immediate, cost-
effective
Patient
Education:
•Provide reliable and
continuous
information, explain
treatment options, or
clarify post-operative
care instructions.
Remote
Monitoring:
•Ensure medication
adherence, and alert
clinicians about
anomalies.
Clinical Decision
Support:
•Data-driven insights
to support clinical
decisions.
Confidentiality and
Compliance:
Ensure that all interactions are
secure and compliant with
healthcare regulations.
26. Relevance to healthcare education
• Adapts to individual student needs
Personalized
Learning:
• Creating diverse and engaging educational materials.
Content Creation:
• Interactive learning experiences (Chatbot)
Student Engagement:
• Provides real-time assessment and feedback .
Assessment and
Feedback:
• content accessible to diverse learners
Accessibility:
• Facilitates collaboration among students and educators,
bridging geographical and language barriers.
Collaboration and
Communication:
27. Personalized Learning
• Tailors educational content
Adaptive Content Delivery:
• Provides instant feedback and real-time assistance
Real-Time Feedback and
Support:
• Engages with interactive dialogues and Simulates scenarios.
Interactive Learning
Environments:
• Analyses - identify strengths and weaknesses for personalized learning.
Data-Driven Insights:
• Adapts content to diverse learners & multiple languages.
Accessibility and Inclusivity:
• Facilitates collaborative learning experiences and peer interactions.
Collaboration and Peer
Interaction:
• Seamlessly integrates with Learning Management Systems (LMS)
Integration with Existing
Platforms:
• Supports lifelong learning and Assists in tracking and maintaining
professional development
Continuous Learning and Skill
Development:
• Ensures ethical guidelines and privacy regulations.
Ethical and Privacy
Considerations:
• Aligns personalized learning experiences and Ensures relevance to real-
world medical practice
Alignment with Healthcare
Objectives:
30. I have been asked
to create a module
for the
examination of the
abdomen for
organomegaly for
medical students.
Create a
curriculum and
include learning
outcomes and the
pedagogy and a
lesson plan
37. Educational videos
• Be concise
• Mobile-compatible
• Optimized for social
media
• Enhance blended
learning Average view time of 1.72 min
(103 Seconds)
38. AI for Video Production
Draft
Learning
Outcomes
LO to Prompt
ChatGPT for
video script
Import/edit
script to AI
Video
Generator
Add
personalised
media
Choose
Voiceover
type
Produce
Review and
Upload
39. Write a script
for the
introduction of
the anatomy of
the
organomegaly
medical student
module. This
will be a 90
second video
script. Just
provide the
narration
41. Assessment and Feedback
• Automated Grading:
• Grading objective assessments (multiple-choice, fill-in-the-blank, etc.)
• Evaluating subjective assessments (short answers, essays) with predefined criteria
• Personalized Feedback:
• Providing tailored feedback on strengths and areas for improvement
• Engaging in interactive dialogues to reinforce learning concepts
• Real-time Support:
• Offering instant feedback on performance
• Available 24/7 for flexible learning schedules
• Data-Driven Insights:
• Tracking performance over time for individual and class insights
• Designing adaptive learning paths based on student needs
• Enhancing Human Interaction:
• Freeing up educators' time for complex student interactions
• Facilitating structured peer review processes
• Ethical and Bias Considerations:
• Ensuring transparency, fairness, and avoidance of biases in AI-driven assessments
44. “the antibiotics used in
leprosy are rifampicin
and streptomycin.
Sometimes you can use
dapsone for resistant
cases. Rifampicin is the
first line drug” - based
on this answer provide
a grade for it
45. AI Tools for RESEARCH
• Elicit for Literature Search
• Scholarcy and Typeset for data extraction and summary
• Genei.io for summarisation and key points highlighting
• Keyword generation with ChatGPT ( targeted prompt engineering)
50. The Art and Science of Qualitative Research
https://tinyurl.com/QUALIRE
Introduction to research in healthcare
https://tinyurl.com/HCARERE
AICHAT BT FOR Research in healthcare
https://tinyurl.com/HCAREREBOT
AI-Powered Academic Writing Write Your Research Paper in a Day
https://tinyurl.com/AIAWRITE
AI CHAT BOT for AI_POWERED ACADEMIC WRITING
https://tinyurl.com/AIAWRITEBOT
Editor's Notes
The clinical domain refers to identifying real‐world clinical needs and validating these needs throughout the life cycle of the project. Herein, the major risks, objectives and key results, and practical advice, across the three time‐phases of development, are presented.
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