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.
Current Work:
- Public Event March 22, 2022 | AI/Machine Learning: Regulation, Development, and Real-World Performance Evaluation
- NEW ARTICLE PUBLISHED | How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support (April 2022 | NEJM Catalyst)
- NEW WHITE PAPER OUT | Evaluating AI-Enabled Clinical Decision and Diagnostic Support Tools Using Real-World Data (March 2022 white paper)
- Understanding Bias and Fairness in AI-enabled Healthcare Software
- Public Event | December 17, 2021 [Video and slides now posted]
- White Paper to follow
Past Work:
- Trust, But Verify: Informational Challenges Surrounding AI-Enabled Clinical Decision Software
- Accountability, secrecy, and innovation in AI-enabled clinical decision software (November 2020 | Journal of Law and the Biosciences)
- AI-Enabled Clinical Decision Support Software: A “Trust and Value Checklist” for Clinicians (November-December 2020 | NEJM Catalyst)
- Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care (January 2019 white paper)
Research Team

Christina Silcox, PhD
Digital Health Policy Fellow
Adjunct Assistant Professor
Senior Team Member
Margolis Core Faculty

Jonathan Gonzalez-Smith, MPAff
Research Associate

Trevan Locke, PhD
Research Associate

Valerie Parker
Policy Analyst

Adam Aten, MPH, MSc
Research Associate

Humphrey Shen
Research Assistant

Arti K. Rai, JD
Elvin R. Latty Professor of Law
Margolis Core Faculty

Thomas Roades, MPP
Policy Analyst