학술논문
Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.
Document Type
Academic Journal
Author
Strauss AT; Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Sidoti CN; Department of Surgery, New York University, Grossman School of Medicine, New York, New York, USA.; Sung HC; Department of Surgery, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Jain VS; Department of Surgery, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Lehmann H; Department of Medicine, Division of Biomedical Informatics & Data Science, School of Medicine, Baltimore, Maryland, USA.; Purnell TS; Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA.; Jackson JW; Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA.; Malinsky D; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA.; Hamilton JP; Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Garonzik-Wang J; Department of Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, Wisconsin.; Gray SH; Department of Surgery, University of Maryland, School of Medicine, Baltimore, Maryland, USA.; Levan ML; Department of Surgery, New York University, Grossman School of Medicine, New York, New York, USA.; Hinson JS; Department of Emergency Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Gurses AP; Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.; Gurakar A; Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Segev DL; Department of Surgery, New York University, Grossman School of Medicine, New York, New York, USA.; Levin S; Department of Emergency Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA.; Beckman Coulter, Brea, California, USA.
Source
Publisher: Wolters Kluwer Health, Inc Country of Publication: United States NLM ID: 101695860 Publication Model: eCollection Cited Medium: Internet ISSN: 2471-254X (Electronic) Linking ISSN: 2471254X NLM ISO Abbreviation: Hepatol Commun Subsets: MEDLINE
Subject
Language
English
Abstract
Background: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions.
Methods: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data.
Results: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS.
Conclusions: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.
(Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.)
Methods: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data.
Results: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS.
Conclusions: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.
(Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.)