Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.

BJR artificial intelligence Pub Date : 2024-03-04 eCollection Date: 2024-01-01 DOI:10.1093/bjrai/ubae003

Usman Mahmood, Amita Shukla-Dave, Heang-Ping Chan, Karen Drukker, Ravi K Samala, Quan Chen, Daniel Vergara, Hayit Greenspan, Nicholas Petrick, Berkman Sahiner, Zhimin Huo, Ronald M Summers, Kenny H Cha, Georgia Tourassi, Thomas M Deserno, Kevin T Grizzard, Janne J Näppi, Hiroyuki Yoshida, Daniele Regge, Richard Mazurchuk, Kenji Suzuki, Lia Morra, Henkjan Huisman, Samuel G Armato, Lubomir Hadjiiski

{"title":"Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.","authors":"Usman Mahmood, Amita Shukla-Dave, Heang-Ping Chan, Karen Drukker, Ravi K Samala, Quan Chen, Daniel Vergara, Hayit Greenspan, Nicholas Petrick, Berkman Sahiner, Zhimin Huo, Ronald M Summers, Kenny H Cha, Georgia Tourassi, Thomas M Deserno, Kevin T Grizzard, Janne J Näppi, Hiroyuki Yoshida, Daniele Regge, Richard Mazurchuk, Kenji Suzuki, Lia Morra, Henkjan Huisman, Samuel G Armato, Lubomir Hadjiiski","doi":"10.1093/bjrai/ubae003","DOIUrl":null,"url":null,"abstract":"<p><p>The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"1 1","pages":"ubae003"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928809/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BJR artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bjrai/ubae003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

查看原文本刊更多论文

人工智能在医学中的应用：通过质量保证、质量控制和验收测试降低风险，实现效益最大化。

人工智能（AI）工具在医学领域的应用给现有的临床工作流程带来了挑战。本评论文章讨论了针对具体情况进行质量保证（QA）的必要性，强调需要采取强有力的质量保证措施，并制定质量控制（QC）程序，其中包括：（1）临床使用前的验收测试（AT）；（2）持续的质量控制监测；（3）充分的用户培训。讨论还包括验收测试和质量控制的基本组成部分，并以实际案例加以说明。我们还强调了我们所认为的制造商或供应商、监管机构、医疗保健系统、医学物理学家和临床医生的共同责任，即进行适当的测试和监督，以确保通过人工智能实现安全、公平的医学变革。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

BJR artificial intelligence

自引率

0.00%

发文量