Artificial Intelligence in Health Care

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: 

 

    Past Work:

     

      July 2022 White Paper | Preventing Bias and Inequities in AI-Enabled Health Tools
      March 2022, NEJM Catalyst Article | How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support
      March 2022, Whitepaper | Evaluating AI-Enabled Clinical Decision and Diagnostic Support Tools Using Real-World Data
      November-December 2020, NEJM Catalyst Article | AI-Enabled Clinical Decision Support Software: A “Trust and Value Checklist” for Clinicians
      November 2020, JLB Article | Accountability, Secrecy, and Innovation in AI-Enabled Clinical Decision Software
      September 2020, White Paper | Trust, But Verify: Informational Challenges Surrounding AI-Enabled Clinical Decision Software
      June 2019, White Paper | Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care

      Research Team

      silcox

      Christina Silcox, PhD

      Research Director, Digital Health
      Adjunct Assistant Professor
      Senior Team Member
      Margolis Core Faculty

      Erin Soule Headshot

      Erin Soule, PhD

      Assistant Research Director

      Valerie Parker Headshot

      Valerie Parker, MS

      Policy Research Associate

      Trevan Locke Headshot

      Trevan Locke, PhD

      Assistant Research Director

      Jonathan Gonzalez-Smith

      Jonathan Gonzalez-Smith, MPAff

      Assistant Research Director

      Thomas Roades Photo

      Thomas Roades, MPP

      Senior Policy Analyst

      Arti Rai

      Arti K. Rai, JD

      Elvin R. Latty Professor of Law
      Margolis Core Faculty