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Review
. 2024 Nov 30;7(1):348.
doi: 10.1038/s41746-024-01300-8.

Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions

Agustina D Saenz  1   2 ; Mass General Brigham AI Governance Committee; Amanda Centi  3 , David Ting  3   4 , Jacqueline G You  3   4 , Adam Landman  3   4 , Rebecca G Mishuris  3   4
Collaborators, Affiliations
Review

Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions

Agustina D Saenz et al. NPJ Digit Med. .

Erratum in

Abstract

This report presents a comprehensive case study for the responsible integration of artificial intelligence (AI) into healthcare settings. Recognizing the rapid advancement of AI technologies and their potential to transform healthcare delivery, we propose a set of guidelines emphasizing fairness, robustness, privacy, safety, transparency, explainability, accountability, and benefit. Through a multidisciplinary collaboration, we developed and operationalized these guidelines within a healthcare system, highlighting a case study on ambient documentation to demonstrate the practical application and challenges of implementing generative AI in clinical environments. Our proposed framework ensures continuous monitoring, evaluation, and adaptation of AI technologies, addressing ethical considerations and enhancing patient care. This work contributes to the discourse on responsible AI use in healthcare, offering a blueprint for institutions to navigate the complexities of AI integration responsibly and effectively, thus promoting better, more equitable healthcare outcomes.

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Conflict of interest statement

Competing interests: A.L. is a consultant for the Abbott Medical Device Cybersecurity Council. A.S. was a consultant for Curai Health during the work for this manuscript and is now a full-time employee.

Figures

Fig. 1
Fig. 1. Development sequence for AI implementation guidelines.
After identifying initial themes with a comprehensive literature review, findings were discussed at a multidisciplinary Executive Leadership Summit. Members of the Executive Leadership identified Core Principles in expert subgroups, ultimately culminating in Official Guidelines.
Fig. 2
Fig. 2. Trends in AI-drafted clinical note.
The Figure 2 displays the average percentage of clinical notes drafted by AI across different medical specialties over time, highlighting that Emergency Medicine specialists tend to retain a higher proportion of AI-generated content in their notes compared to those in Internal Medicine.
Fig. 3
Fig. 3. Feedback over time.
The Figure 3 shows the daily average feedback scores given to the AI-generated notes, providing insights into the perceived quality and utility of AI assistance in clinical documentation.
See this image and copyright information in PMC

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