About this Research Topic

Background

Health systems seeking to integrate internally or externally built AI software into clinical care are navigating complex decisions with minimal regulatory oversight and ample opportunity for harm. The Food and Drug Administration (FDA) has released guidance documents and proposed frameworks applicable to AIS in healthcare, including guidance for determining when software is subject to FDA jurisdiction, guidance for managing regulated and non-regulated software functions together as a system, and a proposed framework for AIS change management, however, there are few resources available on the application of FDA’s guidance. The FDA also recently called for the surfacing and harmonizing of Good Machine Learning Practice, but no structured mechanism to surface best practices has emerged.

This Research Topic fills an urgent gap in the literature by soliciting manuscripts from health systems that are actively engaged in the integration of AIS into clinical care. The insights presented by authors will inform regulators shaping AI policy, engineering teams building products, and health systems attempting to navigate AIS integration in a responsible fashion. The collection will provide an opportunity for health system teams to share learnings and insights that are not often described in the academic literature, but are core components to safe and responsible innovation.

This Research Topic is soliciting contributions from interdisciplinary teams that have successfully integrated AI software into healthcare delivery settings. Authors are asked to describe, in detail, components of the AI software design, development, integration, and evaluation process that are rarely described in the academic literature. The manuscripts are meant to identify challenges and opportunities and are not required to present primary data related to clinical or operational impact. Manuscripts may focus on the following topics:

-Setting and problem assessment
-Internal and external scan of AIS solutions and workflows
-Clinical workflow design and development
-Technology infrastructure design and development
-AIS evaluation leading to clinical integration, change management and governance
-AIS product lifecycle management, including model monitoring and updating.

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Note for authors: As part of the editorial team, Mr. David Vidal and Ms. Sylvia Trujillo will act as Expert Advisors during the peer-review process of submitted contributions and will not take on editorial assignments.

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Dr. Karandeep Singh has received grant funding from Blue Cross Blue Shield of Michigan and Teva Pharmaceuticals. All other Topic Editors declare no conflicts of interest.

Keywords: machine learning, innovation, product development, health policy, digital health

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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