Artificial Intelligence in Health Care

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. 

• New Health Affairs Forefront Article (June 20224) | Best Practices For AI In Health Insurance Claims Adjudication And Decision-Making
• Perspective Article (April 2024) | In a perspective article in npj Digital Medicine, 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 10 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
  • January 2020 | Public Event
  • September 2020 | White Paper
• 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)