Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.
Hepatol Commun
; 7(10)2023 10 01.
Article
en En
| MEDLINE
| ID: mdl-37695082
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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Trasplante de Hígado
/
Sistemas de Apoyo a Decisiones Clínicas
Tipo de estudio:
Clinical_trials
/
Guideline
/
Prognostic_studies
/
Qualitative_research
Límite:
Humans
Idioma:
En
Revista:
Hepatol Commun
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos