Artificial Intelligence in Health Care Portfolio

Portfolio Overview

The field of artificial intelligence is rapidly growing and evolving. Artificial intelligence (AI) refers to the ability of a machine to perform a task that is normally done by humans, including problem-solving and learning. AI-enabled software can be divided into two categories, with the classification depending on how the software is developed. Rules-based algorithms use expert-derived rules to turn the inputs into an output, through a defined and logical process. Data-based algorithms are given sets of labeled input data (called “training data”) and use programmed processes to derive relationships between the inputs and the labels. The relationships can then be used to predict how new input data would likely be labeled. While forms of clinical decision support (CDS) software has been available for many decades, recent advances in data-based AI may have the potential to significantly improve software performance, opening to the door to an explosion of new products, some of which are already in the market. With this explosion will come a host of regulatory, implementation, and adoption challenges in the near- and long-term. 

• Talk | "Navigating the AI Health Policy Landscape: 2024 and Beyond" (December 2024) A featured guest on UCSF CoDEx’s Innovations at the Intersection of AI and Diagnosis Webinar Series, Christina Silcox, Research Director of Digital Health at Duke-Margolis, discussed the transformative policy changes and trends that shaped AI in health care in 2024. Her deep dive explored the ways industry is affected by the roles and regulatory actions federal agencies and state-level initiatives, and key issues such as transparency, equity, and innovation.

• Project | Health System Governance of AI A comprehensive governance system for AI in health care has multiple advantages, including ensuring patient safety, maintaining ethical standards, ensuring regulatory compliance, fostering trust through transparency and accountability, and managing privacy concerns and other legal issues. But AI governance is a relatively new concept for health systems. This project brought together early movers in this space to examine the benefits, challenges, and process in building AI governance into health systems

  • Whitepaper | AI Governance in Health Systems: Aligning Innovation, Accountability, and Trust (November 2024)

  • Webinar | From Principles to Practice: Exploring AI Governance in Health Systems (November 2024) 

• Event | Health AI and Health Policy—Charting a Path for Patients and Progress (October 2024) This event explored the future of AI in healthcare by identifying promising use cases and discussing the importance of balancing innovation with safety and ensuring real-world clinical impact. A panel of federal agency leaders addressed how emerging government policies are advancing the safe and equitable implementation of AI in health care.

• New Health Affairs Forefront Article | Best Practices For AI In Health Insurance Claims Adjudication And Decision-Making (June 2024)

• NPJ Digital Medicine Perspective |  The Potential for Artificial Intelligence to Transform Healthcare: Perspectives from International Health Leaders (April 2024) | Duke-Margolis researchers describe four priority action areas recommended by international health leaders to fully realize the potential of artificial intelligence (AI) in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.
• FDA Convening | Understanding AI/ML in the Drug Development Lifecycle (December 2022)
• AAMI News Article | Moving Ahead of the Pack: Understanding Health System Priorities on AI-Enabled Clinical Decision Support (June 2022)
• White Paper and Public Event | Understanding Bias and Fairness in AI-enabled Healthcare Software
  • Public Event (December 17, 2021)
  • White Paper | Preventing Bias and Inequities in AI-Enabled Health Tools (July 2022)
• NEJM Catalyst Article | How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support (April 2022)
• Public Event | AI/Machine Learning: Regulation, Development, and Real-World Performance Evaluation (March 22, 2022)
• White Paper | Evaluating AI-Enabled Clinical Decision and Diagnostic Support Tools Using Real-World Data (March 2022)
• White Paper and Public Event | Trust, But Verify: Informational Challenges Surrounding AI-Enabled Clinical Decision Software
  • Public Event (January 2020)
  • White Paper (September 2020)
• Journal of Law and the Biosciences Article | Accountability, secrecy, and innovation in AI-enabled clinical decision software (November 2020)
• NEJM Catalyst Article | AI-Enabled Clinical Decision Support Software: A “Trust and Value Checklist” for Clinicians (November-December 2020)
• White Paper | Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care (January 2019)