减少医疗人工智能中的偏见：白皮书。

IF 2.2 3区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Health Informatics Journal Pub Date : 2024-10-01 DOI:10.1177/14604582241291410

Carolyn Sun, Shannon L Harris

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引用次数: 0

摘要

目的：要想改善健康状况，就必须减少人工智能（AI）中的种族主义，但对于如何做到这一点，目前还没有达成共识。方法：在 2022 年的一次国际会议上，专家们齐聚一堂，讨论减少医疗人工智能中偏见的策略。结果：本文阐述了这些策略及其相应的优缺点，并回顾了有关这些策略的现有文献。结论会议提出了五大主题：减少数据集偏差、现有数据的精确建模、人工智能的透明度、人工智能及其开发人员的监管，以及让利益相关者参与讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Reducing bias in healthcare artificial intelligence: A white paper.

Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.

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来源期刊

Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS

CiteScore

7.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.