CONV2X CME 2023

What challenges are presented by AI adoption in healthcare? What role should AI governance play in regard to the opportunities and risks for generative AI (large language models like ChatGPT-4, etc.) in health? How do we responsibly evaluate new technology and ensure guardrails for its deployment, but without stifling innovation? How do we address societal fears and biases? What level of reliability and robustness should algorithms achieve before being adopted with confidence across healthcare? How can AI support health equity and expand access? We will explore these questions and more with some of the nation’s leading experts on these topics.

Learning Objectives

• Understand key opportunities and challenges for AI governance in healthcare, particularly in regard to rapidly emerging generative AI applications
• Discuss various responsible AI and AI governance frameworks that seek to ensure safety and efficacy in AI adoption
• Explore the role of government, legislation, and regulators in supporting safe and equitable adoption of AI in healthcare
• Explore opportunities for government, industry, and academic partnerships to accelerate AI validation, development, and adoption of safety standards
• Consider the potential impact of AI on the healthcare workforce and provider and patient experiences
• Identify the risks we should be most concerned with and what approaches can we take to mitigate those risks?
• By the end of the presentation, participants should have a sense of the opportunities and challenges of adopting AI in healthcare, key risks and frameworks for mitigating them, and the potential role of government, academia, and industry in addressing these issues
  

Generative AI application examples

• Patient Communication and Engagement
• Intelligent chatbots interacting with patients, answering their queries, providing health advice, and helping them manage their conditions
• Mental Health Support
• Provide psychological support to patients, helping them manage stress, anxiety, depression, and other mental health issues. Provide interventions based on CBT and other therapeutic techniques
• Automated Medical Documentation
• Note-taking can allowing doctors to focus more on patient care
• Education and Training
• Create educational content and personalized training for medical students and healthcare professionals, potentially simulating things like patient cases and providing guidance on diagnosis, etc.

Synthesis and Summarization of Medical Research

• Make it easier for people to keep up with the latest developments
• Public Health Communication
• In a public health emergency, help craft health messages, FAQs, and timely and accurate updates for the public
• Generative AI challenges and limitations
• Ensuring the accuracy of AI-generated information
• Maintaining patient privacy
• Handling the ethical implications of AI and human-machine interactions in healthcare
• “Hallucinations” (false or inaccurate information) can occur when model has limited knowledge of a particular domain
• “Distraction” (losing track of earlier inputs) can occur when inputs go beyond allowable token count for model “context window” and models start to produce responses not relevant to queries and context.

Calvin Lawrence, Distinguished Engineer – Responsible AI , Member of AI Ethics Board and Academy of Technology, IBM
Seth Dobrin, PhD, Founder and CEO, Qantm AI 
Gil Alterovitz, PhD, Director, Biomedical Cybernetics Laboratory Harvard Medical School, Member of CHAI
Yoav Schlesinger Architect, Ethical AI Practice, Healthcare Salesforce
Dennis Chornenky, Chief AI Advisor UC Davis Health and CEO, Domelabs AI, Moderator

This panel will explore the exciting potential of telehealth for women’s health, and how innovation is transforming the field. Join industry leaders discuss the future of women’s telehealth and innovative solutions that are unlocking better care for women, including how patients connect with providers for virtual consultations, receive prescriptions, order birth control, and access at-home STI testing.

Learning objectives
• Learn how telehealth is used to improve access to women’s health services, including OB/GYN care and family planning in a hybrid environment using an integrated platform
• Learn how telehealth provides personalized care to women through a comprehensive, holistic approach that addresses physical and mental health
• Explore telehealth support for new mothers with breastfeeding, including virtual lactation consultations and personalized care plans

Bronwyn Harris, CEO, Carbon Health
Andrea Ippolito, CEO, and Founder SimpliFed
Aditi Joshi, Founder, Nagamed Digital Consulting
Matt Sakumoto, Professor, USCF, Moderator

How can community health professionals connect with resident health consumers to encourage positive self care? This goes beyond encouraging physical activity to encompass a range of life style behavior changes with a tailored approach that engages those least engaged with health and wellness and potentially most at risk from acute health risks and longer term conditions such as Type 2 Diabetes. Is the answer purely digital, or does a network of health service managers help drive engagement and better outcomes? This session explores how a data driven, dynamically adaptive approach and adding an “administrator portal” shapes targeted incentives and messaging, adjusting parameters of rewards and behavioral triggers for personalized content. The speaker will present a case use from Hounslow and Buckinghamshire in the UK, where healthcare is optimized through self learning systems.

Learning objectives:
• Insight into own practices and the balance between acute and preventative care
• Understand how smartphone technology, combined with incentives, can be used to engage community residents in their own self care
• Learn how to baseline existing behavior and measure change over time
• Gain insights from smartphone derived data and understand how it can be used to identify barriers to change

Hannah McCarthy, COO, BetterPoints Ltd

Case Use 1:
 How data is stored, shared and monetized connecting an entire ecosystem with benefits.

Case Use 2:
EHRs, genomic DNA, wearables, pharmacy and social determinants of health data and accelerated access to de-identified, tokenized, real-time data.

Dr. Igor Korolev, Digital Health Consultant
Stephan Manoryk Community Leader DeHealth
Ardy Arianpour, CEO, Seqster CEO
Jim St. Claire, COO & Advisor, Multiple Companies, Moderator

In our rapidly advancing technological landscape, artificial intelligence (AI) has emerged as a powerful tool with the potential to revolutionize healthcare, telehealth, and medicine. However, alongside its immense potential come critical ethical considerations that must be addressed. Delve into the ethical dimensions of AI in these domains and the complex challenges and dilemmas that arise through real-world case studies highlighting the
ethical implications of AI in healthcare delivery, telehealth services, and medical decision-making, ethical challenges of using AI algorithms in diagnosing diseases, determining treatment plans, patient privacy and informed consent, and the socioeconomic impact of AI in healthcare, such as job displacement, access to care, and disparities in health outcomes.

Further, panelists will examine existing ethical frameworks and explore the challenges of integrating AI into existing healthcare systems while maintaining human-centered care and preserving the doctor-patient relationship.

Learning Objectives

• Understand the ethical challenges and considerations that arise from the integration of artificial intelligence (AI) in healthcare, telehealth, and medicine.
• Examine real-world case studies highlighting the ethical implications of AI in diagnosing diseases, determining treatment plans, and ensuring patient privacy and informed consent
• Explore the potential socioeconomic impact of AI in healthcare, including job displacement, access to care, and disparities in health outcomes
• Gain insights into the importance of developing responsible AI systems in healthcare that prioritize patient well-being, equity, and privacy
• Discuss the need for transparent and explainable AI algorithms, as well as the ethical considerations surrounding data privacy and security in the context of telehealth and medical records
• Understand the significance of multidisciplinary collaboration involving healthcare professionals, technologists, policymakers, and ethicists to ensure the ethical and responsible integration of AI in healthcare.
• Examine existing ethical frameworks and guidelines applicable to AI in healthcare and explore their implications for preserving human-centered care and the doctor-patient relationship.
• Foster critical thinking and encourage active participation in shaping ethical guidelines and policies surrounding AI in healthcare, telehealth, and medicine.
• Promote a future where AI technologies are harnessed ethically to enhance healthcare delivery, improve health outcomes, and prioritize the well-being of individuals and communities.
• By the end of the talk, attendees will have a comprehensive understanding of the ethical challenges and opportunities surrounding AI in healthcare, telehealth, and medicine, empowering them to navigate the complex intersection of AI and ethics and contribute to the responsible and ethical development of AI technologies in the field of health.
  

Case Examples:

• Medical image analysis: AI algorithms have been developed to analyze medical images such as X-rays, CT scans, and MRIs. These algorithms can assist radiologists in detecting abnormalities, identifying tumors, and providing more accurate diagnoses.
• Predictive analytics for disease diagnosis: AI models can analyze vast amounts of patient data to identify patterns and risk factors associated with various diseases. This can help in early detection, prediction, and prevention of conditions such as cardiovascular diseases, cancer, and diabetes.
• Virtual assistants and chatbots: AI-powered virtual assistants and chatbots are being utilized in healthcare settings to provide personalized patient support, answer common medical queries, and offer triage services. These technologies help streamline patient interactions, reduce wait times, and provide access to healthcare information.
• Drug discovery and development: AI algorithms can analyze large datasets and accelerate the process of drug discovery and development. By predicting the efficacy and safety of potential drug compounds, AI can help identify promising candidates for further investigation, potentially speeding up the delivery of new treatments.
• Remote patient monitoring: AI-enabled wearable devices and sensors can continuously monitor vital signs, activity levels, and other health parameters remotely. This allows healthcare providers to track patient health in real-time, detect changes or anomalies, and provide timely interventions.
• Precision medicine and personalized treatment plans: AI can analyze genomic data and clinical information to provide personalized treatment recommendations based on an individual’s unique characteristics. This approach supports precision medicine, tailoring therapies to maximize effectiveness and minimize side effects.
• Health risk assessment and population health management: AI algorithms can analyze population-level health data to identify trends, predict disease outbreaks, and assess health risks within specific communities. This information can aid in resource allocation, targeted interventions, and public health planning.

Asha Saxena, Founder and CEO, Women Leaders In Data & AI (WLDA)
Dr. Keng-Yen Huang, Associate Prof, Population Health & Child and Adolescent Psychiatry, NYU School of Medicine
Cortnie Abercrombie, CEO and Founder, AI Truth
Dr. Besa H. Bauta, Adj. Assistant Professor, New York University

Tune into this dynamic session on Exponential Leaps in Medicine with XR, and discover how XR technologies are revolutionizing healthcare, from surgical simulations to patient care. Explore the regulatory challenges and clinical solutions associated with adopting XR in medicine. Gain insights from the speakers on their experiences and leave inspired by the transformative potential of XR in healthcare.

Learning objectives
• Provide an overview of the current state and applications of medical extended reality (XR) technologies in healthcare.
• Discuss the benefits and challenges of the state of XR in medicine.
• Explore the future potential and ethical considerations of medical XR in healthcare.
• Gain insights on how medical XR can be used in current and future healthcare environments.
• Understand regulatory frameworks that could be put in place to ensure consumers and end-users are appropriately protected.

Mark Zhang, DO, MMSc, FAMIA, Associate Chief Medical Information Officer – Digital Innovation Brigham and Women’s Hospital
Dan Scarf, CEO, XRAI Glass
Claude Pirtle, CMIO, Walmart Health, Moderator

How do we evaluate large language models for use in healthcare? What are the trusted frameworks for value assessment? Is automation through GPT successfully addressing the healthcare administrative and workforce crisis? What about clinical decision making? Expect a candid discussion from these experts on the implications of LLM’s and AI in Primary Care in the US and around the globe.

Learning Outcomes
• Acquire knowledge of the fundamental principles of large language models
• Understand how these models are trained and deployed in a healthcare context
• Learn how to assess the efficiency and effectiveness of large language models in healthcare
• Understand the measures used in determining the success of these models, such as improved patient outcomes, efficiency in operations, and patient satisfaction
• Learn to apply evaluation or translational frameworks in assessing the value of large language models
• Gain insights into how automation, specifically through GPT, is helping to solve healthcare administrative challenges
• Learn about the use of large language models in clinical decision-making
• Understand the strengths and limitations of these models in making clinical decisions, administration, and the ethics

Sandeep Reddy,Director, MBA(Healthcare Management), Deakin School of Medicine
and Chairman, Medi-AI
Alexandre Lebrun, CEO, Nabla
Adam Chee, PhD, Chief, Smart Health Leadership Centre, Institute of Systems Science National University of Singapore
Dimitris Kalogeropoulos, CEO, Global Health and Digital Innovation Foundation, Moderator

Governments and technology companies are increasingly collecting vast amounts of personal data, prompting new law, myriad investigations, and calls for stricter regulation to protect individual privacy. Yet despite these issues, economics tells us that society needs and is demanding more data sharing rather than less, because the benefits of publicly available data often outweigh the costs. Better economic data could vastly improve policy responses to the next crisis. Data increasingly powers innovation, and it needs to be used for the public good, while individual privacy is protected. This is new and unfamiliar terrain for policymaking, and it requires a careful approach. The pandemic has brought the increasing dominance of big, data-gobbling tech companies into sharp focus. From online retail to home entertainment, digitally savvy businesses are collecting data and deploying it to anticipate product demand and set prices, lowering costs and outwitting more traditional competitors. This session features three experts who will discuss the challenges of balancing citizen and patient privacy with open data sharing for the public good and improved population health.

Learning objectives
• Explore how open data can improved efficiencies and reduced costs
• Learn how increased transparency can increase accountability and lead to less corruption
• Consider how open data has the potential to bring people together who are working on similar
issues who can exchange ideas, findings, discuss challenges, and encourage data collaboration
rather than competitiveness
• Appreciate the hazards and costs for incorrect use of data and missing data

Lorenzo Cristofaro, Partner, Panetta Law
Dr. Paul Barach, Professor, Thomas Jefferson University
Dr. Osama El-Hassan, Health Informatics Specialist, Dubai Health Authority