Implementing AI In Wearable Health Apps For Better TomorrowJai Mehta
For practical wearables and Internet of Things (IoT) implementations, Artificial Intelligence studies specific problem solving or reasoning tasks. Healthcare mobility solutions boost capabilities such as visual perception, speech recognition, and decision-making.
But wearables and the Internet of Things (IoT) work without an AI engine, why do we need it in the first place. Because the true value lies in insights.
Artificial intelligence is being used increasingly in healthcare to analyze large amounts of complex data and patient information. AI applications have been developed for tasks like diagnosing diseases, developing treatment protocols, and monitoring patients. This allows for faster, more accurate, and unbiased analysis compared to humans. While AI can benefit healthcare in many ways, it also faces challenges in cost, privacy concerns, and potential job losses. If implemented effectively, AI is predicted to significantly improve health outcomes and save billions of dollars globally by 2026 through more efficient, personalized care.
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
Artificial Intelligence In Medical IndustryDataMites
The document discusses the use of artificial intelligence and machine learning in the medical industry. It describes how AI can be used to analyze and understand complex medical data, aiding in tasks like cancer diagnosis, drug development through protein folding, and detecting heart diseases using smartwatches. The document also lists several other medical applications of AI such as diagnostic decision support, self-diagnosis through AI doctors, monitoring medication usage, detecting hospital infections through computer vision, and using AI to treat social anxiety.
Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."
Ai idea to implementation : Use cases in Healthcare Swathi Young
AI and machine learning are transformative technologies that have the potential to disrupt status quo, enhance innovation, and reduce operational costs in organizations. This presentation provides a high level overview of the important steps to consider when implementing an AI system along with use cases in the healthcare sector.
Smart health prediction using data mining by customsoftCustom Soft
CustomSoft India based software company developed wonderful software for Smart Health prediction using Data Mining for its esteemd Ckients from US, UK, Canada, Singapore, South Africa based clients
The document discusses making clinical terminology work by driving in-situ use of complex classification schemes using commoditized software. It notes that clinical classification involves many "wicked" influences that make the problem difficult to solve. The author proposes embedding standardized clinical terminologies into commonly used software like Microsoft Word to facilitate electronic documentation and data extraction in a way that is usable for both humans and machines without requiring technical expertise. A demonstration is provided of how standardized terminologies and other services could be integrated into word processing to support clinical documentation and decision-making.
Implementing AI In Wearable Health Apps For Better TomorrowJai Mehta
For practical wearables and Internet of Things (IoT) implementations, Artificial Intelligence studies specific problem solving or reasoning tasks. Healthcare mobility solutions boost capabilities such as visual perception, speech recognition, and decision-making.
But wearables and the Internet of Things (IoT) work without an AI engine, why do we need it in the first place. Because the true value lies in insights.
Artificial intelligence is being used increasingly in healthcare to analyze large amounts of complex data and patient information. AI applications have been developed for tasks like diagnosing diseases, developing treatment protocols, and monitoring patients. This allows for faster, more accurate, and unbiased analysis compared to humans. While AI can benefit healthcare in many ways, it also faces challenges in cost, privacy concerns, and potential job losses. If implemented effectively, AI is predicted to significantly improve health outcomes and save billions of dollars globally by 2026 through more efficient, personalized care.
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
Artificial Intelligence In Medical IndustryDataMites
The document discusses the use of artificial intelligence and machine learning in the medical industry. It describes how AI can be used to analyze and understand complex medical data, aiding in tasks like cancer diagnosis, drug development through protein folding, and detecting heart diseases using smartwatches. The document also lists several other medical applications of AI such as diagnostic decision support, self-diagnosis through AI doctors, monitoring medication usage, detecting hospital infections through computer vision, and using AI to treat social anxiety.
Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."
Ai idea to implementation : Use cases in Healthcare Swathi Young
AI and machine learning are transformative technologies that have the potential to disrupt status quo, enhance innovation, and reduce operational costs in organizations. This presentation provides a high level overview of the important steps to consider when implementing an AI system along with use cases in the healthcare sector.
Smart health prediction using data mining by customsoftCustom Soft
CustomSoft India based software company developed wonderful software for Smart Health prediction using Data Mining for its esteemd Ckients from US, UK, Canada, Singapore, South Africa based clients
The document discusses making clinical terminology work by driving in-situ use of complex classification schemes using commoditized software. It notes that clinical classification involves many "wicked" influences that make the problem difficult to solve. The author proposes embedding standardized clinical terminologies into commonly used software like Microsoft Word to facilitate electronic documentation and data extraction in a way that is usable for both humans and machines without requiring technical expertise. A demonstration is provided of how standardized terminologies and other services could be integrated into word processing to support clinical documentation and decision-making.
Augmenting Health care delivery in Generative AI era: Balancing the hope and ...JAI NAHAR, MD MBA
1) The document discusses the potential for generative AI to augment healthcare delivery while balancing hype with realistic expectations.
2) Some potential applications of generative AI discussed include intelligent digital assistants, medical record summarization, clinical decision support, and tools to enhance patient experience.
3) Challenges that could limit adoption include issues around reliability, bias, privacy and a lack of guidance on appropriate use and governance. Collaboration across stakeholders is advocated to address challenges and responsibly develop applications.
Role of NLP, Conversational AI & Virtual Voice Assistants in PediatricsJAI NAHAR, MD MBA
This document discusses the role of natural language processing (NLP), conversational AI, and virtual voice assistants in pediatrics. It begins with an introduction to NLP and how it allows computers to understand spoken and written human language. It then discusses several use cases for clinical NLP, including automation of workflows, analytics, prediction, and conversational agents. Examples of chatbots and virtual assistants currently used in healthcare are provided. The document outlines the current state of conversational AI and envisions future directions such as multimodal data fusion to create contextual AI, integration of CAI into physician workflows, and use of hybrid technologies combining CAI with augmented reality and robotics. It concludes that NLP can unlock insights from unstructured data, CAI provides
Role of Conversational AI and Virtual Voice Assistants in Cardiology: What is...JAI NAHAR, MD MBA
With the advancements in Voice technology and Natural language processing, Conversational AI and Virtual Voice Assistants are gaining increasing attention in health care for developing provider, patient and enterprise facing solutions. This talk will focus on Conversational AI, Virtual voice assistants and their applications in health care delivery.
Artificial intelligence in Healthcare by Dr. Laila AzmiLaila Azmi Maqbool
Artificial intelligence has become an important topic in healthcare. AI can help improve preventative care and make people healthier. It allows for easier decision making by having a digital "friend" to assist. While human intelligence has advantages like creativity and emotion, AI offers benefits like high-speed information processing and built-in memory. AI can be applied in traditional and complementary medicine for tasks like lifestyle management, diagnostics, purification procedures, and herbal medication formulations. However, AI also faces challenges like concerns over job loss, an inability to provide human care and empathy, and issues around data privacy and security. Overall, AI has great potential to transform and improve many areas of healthcare if these challenges can be addressed.
AI Design in High Risk Settings - Aligning business impact, risks, and innovation
As AI becomes integral to business strategy, many organizations have struggled to navigate the delicate balancing act between technical complexity and business value, especially with the advent of generative AI. This is especially true in high-risk settings like healthcare, defense, and financial services.
In this session, actionable strategies for enhancing AI and data literacy among stakeholders, aligning AI projects with business objectives, and fostering trust between AI builders and users will be explored. While many strategies are applicable to both traditional machine learning and generative AI projects, special considerations for projects incorporating generative AI will also be explored.
Attendees will walk away with practical insights to propel AI projects from the design board to real-world impact and pave the way for a culture of informed and responsible AI innovation within their organizations.
Session Outline:
Participants will learn how to:
· Develop greater AI and data literacy among stakeholders and users
· Align between business outcomes and AI project goals
· Build trust and effective communication channels between AI builders and users
· Identify and communicate the potential risks and unintended consequences that arise from both traditional and generative ML models
Background Knowledge:
Suitable for all backgrounds - especially those looking to communicate more effectively with stakeholders, or stakeholders learning to get more comfortable with technical considerations.
Role of artificial intelligence in health carePrachi Gupta
Artificial intelligence has many applications in healthcare, including improving disease diagnosis through analysis of medical imaging and other patient data, aiding radiologists in detecting abnormalities, and enabling constant remote patient monitoring. The use of AI is expected to lower medical costs through greater accuracy and better predictive analysis. It is being applied to issues like managing the coronavirus outbreak through monitoring patients and regulating hospital visitor flow. Going forward, AI may help predict where virus outbreaks are likely to occur.
Emerging Frontier in Cardiovascular Care: Conversational AI & Virtual Voice A...JAI NAHAR, MD MBA
This presentation will focus on Conversational AI, Virtual voice assistants, their potential uses in augmenting cardiovascular care, and challenges in their adoption.
The MD Anderson / IBM Watson Announcement: What does it mean for machine lear...Health Catalyst
It’s been over six years since IBM’s Watson amazed all of us on Jeopardy, but it has yet to deliver similar breakthroughs in healthcare. The headlines in last week’s Forbes article read, “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.” Is it really a setback for the entire industry or not? Health Catalyst’s EVP for Product Development, Dale Sanders, believes that the challenges are unique to IBM’s machine learning strategy in healthcare. If they adjust that strategy and better manage expectations about what’s possible for machine learning in medicine, the future will be brighter for Watson, their clients, and AI in healthcare, in general. Watson’s success is good for all of us, but it’s failure is bad for all of us, too.
Join Dale as he discusses:
The good news: Machine learning technology is accelerating at a rate beyond Moore’s Law. Dale believes that machine learning algorithms and models are doubling in capability every six months.
The bad news: The healthcare data ecosystem is not nearly as rich as many would believe, and certainly not as rich as that used to train Watson for Jeopardy. Without high-volume, high-quality data, Watson’s potential and the constant advances in machine learning algorithms will hit a glass ceiling in healthcare.
The best news: By adjusting strategy and expectations, there are still plenty of opportunities to do great things with machine learning by using the current data content in healthcare, while we build out the volume and breadth of data we need to truly understand the patient at the center of the healthcare picture… and you don’t need an army of PhD data scientists to do it.
Infervision is a company that uses artificial intelligence to help doctors by automatically recognizing symptoms on medical images and recommending treatments. Their goal is to make top medical expertise available to everyone by reducing the burden on doctors and improving access to healthcare in rural areas. They have developed powerful AI models for various diseases by combining deep learning with medical data from partner hospitals in China. Their products help generate diagnostic reports and can screen for diseases to improve efficiency and lower healthcare costs.
Digital health care technology is transforming hospitals. While technology offers opportunities to improve quality, safety and efficiency, fully digitizing healthcare and replacing clinical judgement with algorithms is still a long way off. Hospitals need to focus on using technology to support, not replace, clinicians. Success requires balancing the needs of people, processes and technology, and managing risks from unintended consequences and legal compliance issues. The ultimate goal remains providing high quality, patient-centered care.
CLGPPT FOR DISEASE DETECTION PRESENTATIONYashRajput82
This document summarizes a project presentation submitted by three group members - Aanchal Rastogi, Kapil Gangwar, and Shahnavaj - to their department of computer science engineering on the topic of "Disease Prognostication & Prevention Using Soft Computing". The presentation includes an introduction, explanations of artificial intelligence and machine learning, the problem statement, evolution of the topic, challenges and limitations, implementations, future work, and a conclusion.
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
AI has made significant advances in medicine and healthcare by transforming how care is delivered, diagnosed, and managed. Some key areas AI is being applied include medical imaging analysis to help detect diseases, using genomic data to identify disease predispositions, and predictive analytics to anticipate patient needs. While AI shows great potential, it is not intended to replace doctors but rather enhance their capabilities and lead to better patient outcomes.
This is the talk I delivered in one of the seminars organised by ASSOCHAM India in partnership with Department of IT and Electronics, Govt. of WB, India.
This document discusses how digital technologies such as voice assistants can transform the patient experience. It outlines how voice assistants can be used across various healthcare settings from home care to inpatient settings. Voice assistants can screen patients, provide medical information, assist with appointments, enable remote monitoring, and support chronic disease management. The document argues that voice assistants should be designed with a patient-centered approach, be easy to use, inclusive, reliable, and protect privacy/security in order to successfully engage patients and improve health outcomes.
Augmenting Health care delivery in Generative AI era: Balancing the hope and ...JAI NAHAR, MD MBA
1) The document discusses the potential for generative AI to augment healthcare delivery while balancing hype with realistic expectations.
2) Some potential applications of generative AI discussed include intelligent digital assistants, medical record summarization, clinical decision support, and tools to enhance patient experience.
3) Challenges that could limit adoption include issues around reliability, bias, privacy and a lack of guidance on appropriate use and governance. Collaboration across stakeholders is advocated to address challenges and responsibly develop applications.
Role of NLP, Conversational AI & Virtual Voice Assistants in PediatricsJAI NAHAR, MD MBA
This document discusses the role of natural language processing (NLP), conversational AI, and virtual voice assistants in pediatrics. It begins with an introduction to NLP and how it allows computers to understand spoken and written human language. It then discusses several use cases for clinical NLP, including automation of workflows, analytics, prediction, and conversational agents. Examples of chatbots and virtual assistants currently used in healthcare are provided. The document outlines the current state of conversational AI and envisions future directions such as multimodal data fusion to create contextual AI, integration of CAI into physician workflows, and use of hybrid technologies combining CAI with augmented reality and robotics. It concludes that NLP can unlock insights from unstructured data, CAI provides
Role of Conversational AI and Virtual Voice Assistants in Cardiology: What is...JAI NAHAR, MD MBA
With the advancements in Voice technology and Natural language processing, Conversational AI and Virtual Voice Assistants are gaining increasing attention in health care for developing provider, patient and enterprise facing solutions. This talk will focus on Conversational AI, Virtual voice assistants and their applications in health care delivery.
Artificial intelligence in Healthcare by Dr. Laila AzmiLaila Azmi Maqbool
Artificial intelligence has become an important topic in healthcare. AI can help improve preventative care and make people healthier. It allows for easier decision making by having a digital "friend" to assist. While human intelligence has advantages like creativity and emotion, AI offers benefits like high-speed information processing and built-in memory. AI can be applied in traditional and complementary medicine for tasks like lifestyle management, diagnostics, purification procedures, and herbal medication formulations. However, AI also faces challenges like concerns over job loss, an inability to provide human care and empathy, and issues around data privacy and security. Overall, AI has great potential to transform and improve many areas of healthcare if these challenges can be addressed.
AI Design in High Risk Settings - Aligning business impact, risks, and innovation
As AI becomes integral to business strategy, many organizations have struggled to navigate the delicate balancing act between technical complexity and business value, especially with the advent of generative AI. This is especially true in high-risk settings like healthcare, defense, and financial services.
In this session, actionable strategies for enhancing AI and data literacy among stakeholders, aligning AI projects with business objectives, and fostering trust between AI builders and users will be explored. While many strategies are applicable to both traditional machine learning and generative AI projects, special considerations for projects incorporating generative AI will also be explored.
Attendees will walk away with practical insights to propel AI projects from the design board to real-world impact and pave the way for a culture of informed and responsible AI innovation within their organizations.
Session Outline:
Participants will learn how to:
· Develop greater AI and data literacy among stakeholders and users
· Align between business outcomes and AI project goals
· Build trust and effective communication channels between AI builders and users
· Identify and communicate the potential risks and unintended consequences that arise from both traditional and generative ML models
Background Knowledge:
Suitable for all backgrounds - especially those looking to communicate more effectively with stakeholders, or stakeholders learning to get more comfortable with technical considerations.
Role of artificial intelligence in health carePrachi Gupta
Artificial intelligence has many applications in healthcare, including improving disease diagnosis through analysis of medical imaging and other patient data, aiding radiologists in detecting abnormalities, and enabling constant remote patient monitoring. The use of AI is expected to lower medical costs through greater accuracy and better predictive analysis. It is being applied to issues like managing the coronavirus outbreak through monitoring patients and regulating hospital visitor flow. Going forward, AI may help predict where virus outbreaks are likely to occur.
Emerging Frontier in Cardiovascular Care: Conversational AI & Virtual Voice A...JAI NAHAR, MD MBA
This presentation will focus on Conversational AI, Virtual voice assistants, their potential uses in augmenting cardiovascular care, and challenges in their adoption.
The MD Anderson / IBM Watson Announcement: What does it mean for machine lear...Health Catalyst
It’s been over six years since IBM’s Watson amazed all of us on Jeopardy, but it has yet to deliver similar breakthroughs in healthcare. The headlines in last week’s Forbes article read, “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.” Is it really a setback for the entire industry or not? Health Catalyst’s EVP for Product Development, Dale Sanders, believes that the challenges are unique to IBM’s machine learning strategy in healthcare. If they adjust that strategy and better manage expectations about what’s possible for machine learning in medicine, the future will be brighter for Watson, their clients, and AI in healthcare, in general. Watson’s success is good for all of us, but it’s failure is bad for all of us, too.
Join Dale as he discusses:
The good news: Machine learning technology is accelerating at a rate beyond Moore’s Law. Dale believes that machine learning algorithms and models are doubling in capability every six months.
The bad news: The healthcare data ecosystem is not nearly as rich as many would believe, and certainly not as rich as that used to train Watson for Jeopardy. Without high-volume, high-quality data, Watson’s potential and the constant advances in machine learning algorithms will hit a glass ceiling in healthcare.
The best news: By adjusting strategy and expectations, there are still plenty of opportunities to do great things with machine learning by using the current data content in healthcare, while we build out the volume and breadth of data we need to truly understand the patient at the center of the healthcare picture… and you don’t need an army of PhD data scientists to do it.
Infervision is a company that uses artificial intelligence to help doctors by automatically recognizing symptoms on medical images and recommending treatments. Their goal is to make top medical expertise available to everyone by reducing the burden on doctors and improving access to healthcare in rural areas. They have developed powerful AI models for various diseases by combining deep learning with medical data from partner hospitals in China. Their products help generate diagnostic reports and can screen for diseases to improve efficiency and lower healthcare costs.
Digital health care technology is transforming hospitals. While technology offers opportunities to improve quality, safety and efficiency, fully digitizing healthcare and replacing clinical judgement with algorithms is still a long way off. Hospitals need to focus on using technology to support, not replace, clinicians. Success requires balancing the needs of people, processes and technology, and managing risks from unintended consequences and legal compliance issues. The ultimate goal remains providing high quality, patient-centered care.
CLGPPT FOR DISEASE DETECTION PRESENTATIONYashRajput82
This document summarizes a project presentation submitted by three group members - Aanchal Rastogi, Kapil Gangwar, and Shahnavaj - to their department of computer science engineering on the topic of "Disease Prognostication & Prevention Using Soft Computing". The presentation includes an introduction, explanations of artificial intelligence and machine learning, the problem statement, evolution of the topic, challenges and limitations, implementations, future work, and a conclusion.
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
AI has made significant advances in medicine and healthcare by transforming how care is delivered, diagnosed, and managed. Some key areas AI is being applied include medical imaging analysis to help detect diseases, using genomic data to identify disease predispositions, and predictive analytics to anticipate patient needs. While AI shows great potential, it is not intended to replace doctors but rather enhance their capabilities and lead to better patient outcomes.
This is the talk I delivered in one of the seminars organised by ASSOCHAM India in partnership with Department of IT and Electronics, Govt. of WB, India.
Similar to Rise of Intelligent Machines: The Potential of Artificial Intelligence in Cardiovascular Care (20)
This document discusses how digital technologies such as voice assistants can transform the patient experience. It outlines how voice assistants can be used across various healthcare settings from home care to inpatient settings. Voice assistants can screen patients, provide medical information, assist with appointments, enable remote monitoring, and support chronic disease management. The document argues that voice assistants should be designed with a patient-centered approach, be easy to use, inclusive, reliable, and protect privacy/security in order to successfully engage patients and improve health outcomes.
Future applications of ChatGPT and MedGPT in healthcare include using them as intelligent electronic health records with summarization abilities, deep computer-aided diagnosis to assist clinicians, and ambient clinical AI to support medical research. For patients, virtual assistants could provide education, help with clinical trials, and act as interpreters. Enterprises could utilize voice bots and assistants for operational efficiency and knowledge management. However, ensuring ethical use through governance frameworks and focusing on societal good and digital inclusion will be important.
Rethinking Conversation in Medicine: Balancing the hype and hope of Generativ...JAI NAHAR, MD MBA
The document discusses the potential for conversational AI in healthcare. It begins with introductory concepts of conversational AI and how it utilizes natural language through voice and text interfaces. The document then discusses potential applications in healthcare like intelligent medical search engines, clinical decision support, and digital assistants to help with appointments. Challenges discussed include privacy, accuracy, regulation and ethics. The document concludes that conversational AI could transform healthcare experiences if developed responsibly and effectively with proper governance and oversight.
Role of Medical Intelligence in Augmenting The Virtual Health Care DeliveryJAI NAHAR, MD MBA
This talk will focus on how Medical intelligence (using ML) gained from virtual health care delivery ecosystem (digital home monitoring devices, sensors, apps, virtual assistants) can facilitate real time actionable insights, promoting prompt risk prediction, mitigation, and personalized prescription for the patient.
This talk focuses on how Medical intelligence (using ML) gained from virtual health care delivery ecosystem (digital home monitoring devices, sensors, apps, virtual assistants) can facilitate real time actionable insights, promoting prompt risk prediction, mitigation, and personalized prescription for the patient.
1) The document discusses how to integrate new technology innovations within healthcare systems using a 6 stage framework: identifying problems/needs, proposing solutions, developing prototypes, piloting, evaluating/iterating, and final launch.
2) Stage 1 involves identifying compelling use cases that have a clear impact and value proposition.
3) Stages 3-4 involve developing prototypes, piloting solutions, and integrating them with workflows while ensuring privacy, usability, and legal compliance.
4) Stages 5-6 focus on refining solutions based on user feedback, fixing issues, realigning with goals, and finally launching at scale with training and champions.
Emerging Frontier in Health Care delivery: Conversational AI & Virtual Voice ...JAI NAHAR, MD MBA
With the advancements in Voice technology and Natural language processing, Conversational AI and Virtual Voice Assistants are gaining increasing attention in health care for developing provider, patient and enterprise facing solutions.
Cognitive personal digital assistant for physiciansJAI NAHAR, MD MBA
The document discusses physician burnout as a major problem, with over 51% of physicians reporting burnout in 2017. The proposed solution is a Cognitive Personal Digital Assistant (CPDA) that can be accessed across devices to help optimize physicians' workflow. The CPDA would decrease clerical burden through features like documentation support, EHR integration, and translation capabilities. It would also help with patient communication, administrative tasks, clinical decision making, knowledge management, and promoting physician wellness. The goal of the CPDA is to decrease daily workload, optimize time management, increase productivity, promote wellness, and restore work-life balance for physicians.
This document discusses using artificial intelligence (AI) to help address challenges in anomalous aortic origin of coronary artery (AAOCA). AAOCA is a leading cause of sudden cardiac death in young athletes. There are knowledge gaps in risk stratification for AAOCA patients. The document proposes a two-step approach using AI: 1) unsupervised machine learning to uncover unknown high-risk phenotypes from clinical data, and 2) supervised learning to develop refined risk stratification models. Challenges include data availability and expertise in machine learning. Future directions include increased data collaboration and human-AI partnerships to advance precision cardiovascular medicine.
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- 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
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...Donc Test
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by Stamler, Verified Chapters 1 - 33, Complete Newest Version Community Health Nursing A Canadian Perspective, 5th Edition by Stamler, Verified Chapters 1 - 33, Complete Newest Version Community Health Nursing A Canadian Perspective, 5th Edition by Stamler Community Health Nursing A Canadian Perspective, 5th Edition TEST BANK by Stamler Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Pdf Chapters Download Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Pdf Download Stuvia Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Study Guide Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Ebook Download Stuvia Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Questions and Answers Quizlet Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Studocu Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Quizlet Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Pdf Chapters Download Community Health Nursing A Canadian Perspective, 5th Edition Pdf Download Course Hero Community Health Nursing A Canadian Perspective, 5th Edition Answers Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Ebook Download Course hero Community Health Nursing A Canadian Perspective, 5th Edition Questions and Answers Community Health Nursing A Canadian Perspective, 5th Edition Studocu Community Health Nursing A Canadian Perspective, 5th Edition Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Pdf Chapters Download Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Pdf Download Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Study Guide Questions and Answers Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Ebook Download Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Questions Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Studocu Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Stuvia
Does Over-Masturbation Contribute to Chronic Prostatitis.pptxwalterHu5
In some case, your chronic prostatitis may be related to over-masturbation. Generally, natural medicine Diuretic and Anti-inflammatory Pill can help mee get a cure.
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptx
Rise of Intelligent Machines: The Potential of Artificial Intelligence in Cardiovascular Care
1. Jai Nahar, MD, MBA
Associate Professor of Pediatrics
George Washington University School of Medicine
Attending Pediatric Cardiology
Children’s National Medical Center, Washington DC
Rise of Intelligent
Machines: The Potential of
Artificial Intelligence in
Cardiovascular Care
3. Cardiovascular care in the age of Artificial
Intelligence (AI)
1. Why should providers care about AI?
2. Why should your patients care about AI?
3. What are the challenges?
5. What AI Technologies are available for
Cardiovascular Care ?
• Computer Vision
• Natural Language Processing (NLP)
• Conversational AI
• Virtual Assistants
• Hybrid Technologies: AR/VR+ AI, Robotics+AI
• Robotic Process Automation (RPA)
6. AI: Provider’s Lens
MD Work
flow
Augmentation
Care
Delivery
Revenue
Optimization
Research
Operational
Speech recognition, NLP for:
• EHR documentation
• EHR Information Extraction
• Compliance (Coding, Quality and Regulatory)
ML, Computer vision, NLP for:
• Clinical Decision support
• Diagnosis
• Prevention
• Risk Stratification
• Early warning system
• Treatment
NLP for:
• Computer assisted
coding
• Automated
preauthorization
• No show prediction
NLP for:
• Clinical trial matching
• Registry reporting
RPA for:
Process Automation
7. Patient’s lens: Can AI help me?
• Reduce Friction in care delivery
• Fill the gaps in care
• Increase access
• Disease Management at Home
• Wellness promotion
8. Patient’s Lens: Conversational AI
Smart Devices, Sensors, Humanized Robots
Home
Conversational AI
(Virtual agents, Chatbots
Smart Sensors
Smart Devices
Hospital
Conversational AI
(Chatbots,
Humanized Robots)
• Appointment Navigation
• Patient Education
• Patient Engagement
• Chronic care management
• Personal Health and Wellness
coach
• Early warning system
• Ambulatory monitoring of
chronic diseases
• Patient Education
• Inpatient care navigation
• Discharge preparation
9. What are the Challenges in AI Adoption?
• Privacy
• Security
• Accuracy
• Reliability
• Trust
• Ethics
• Design and development
10. Need for Balanced Outlook
Avoid AI
Phobia
Avoid AI
Hype
Augmented
Cardiologist
11. Closing Thoughts
Man + Machine synergy = Strengthens the power of healing
Team effort: Collaboration of all stakeholders with shared vision and aligned interests is key to
excel.
With the rise of Machine Intelligence, it is opportune time to consider the impact from provider’s and patient’s perspectives.
There are few important questions which need to be considered which will help in adoption of this important technology and scale it up.
These 3 questions are:
Why should providers care about AI?
Why should your patients care about AI?
What are the challenges?
Per American cognitive scientist Marvin Minsky: “The science of making machines do things that would require intelligence if done by man”.
Looking at AI from providers lens: Use of AI for providers can be divided amongst various functional groups, as illustrated in this figure.
As a patient my important question is : Can AI help me.
Yes, AI can help to :
From patient’s perspective we can also look at possible uses of AI in setting of patients home and at Hospital.
At home Conversational AI agents ca help in ----------
Additionally at home use of smart sensors and devices can facilitate EWS and ambulatory monitoring of chronic diseases.
In hospital setting the Conversational AI agents such as Chatbots and Humanized robots can help in --------
In current Era of Augmented Intelligence (Human + Machine intelligence) it is important to have a balance outlook by avoiding both AI phobia and AI hype.