AI Governance in Healthcare: Best Practices, Solutions, and Unresolved Issues
Authors: Lawrence Calvin , Dobrin, PhD Seth , Alterovitz, PhD Gil , Schlesinger Yoav , Chornenky Moderator: Dennis 
Published: September 2024

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.