Artificial Intelligence in Medicine: Mitigating Risks and Maximizing Benefits Via Quality Assurance, Quality Control, and Acceptance Testing

Overview

Journal BJR Artif Intell

Date 2024 Mar 13

PMID 38476957

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 Nappi

Hiroyuki Yoshida

Daniele Regge

Richard Mazurchuk

Kenji Suzuki

Lia Morra

Henkjan Huisman

Samuel G Armato 3rd

Lubomir Hadjiiski

Affiliations

Soon will be listed here.

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.

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