Goh Serene, Goh Rachel Sze Jen, Chong Bryan, Ng Qin Xiang, Koh Gerald Choon Huat, Ngiam Kee Yuan, Hartman Mikael
Department of Surgery, National University Hospital, Singapore, Singapore.
Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore.
J Med Internet Res. 2025 May 15;27:e62941. doi: 10.2196/62941.
Artificial intelligence (AI) studies show promise in enhancing accuracy and efficiency in mammographic screening programs worldwide. However, its integration into clinical workflows faces several challenges, including unintended errors, the need for professional training, and ethical concerns. Notably, specific frameworks for AI imaging in breast cancer screening are still lacking.
This study aims to identify the challenges associated with implementing AI in breast screening programs and to apply the Consolidated Framework for Implementation Research (CFIR) to discuss a practical governance framework for AI in this context.
Three electronic databases (PubMed, Embase, and MEDLINE) were searched using combinations of the keywords "artificial intelligence," "regulation," "governance," "breast cancer," and "screening." Original studies evaluating AI in breast cancer detection or discussing challenges related to AI implementation in this setting were eligible for review. Findings were narratively synthesized and subsequently mapped directly onto the constructs within the CFIR.
A total of 1240 results were retrieved, with 20 original studies ultimately included in this systematic review. The majority (n=19) focused on AI-enhanced mammography, while 1 addressed AI-enhanced ultrasound for women with dense breasts. Most studies originated from the United States (n=5) and the United Kingdom (n=4), with publication years ranging from 2019 to 2023. The quality of papers was rated as moderate to high. The key challenges identified were reproducibility, evidentiary standards, technological concerns, trust issues, as well as ethical, legal, societal concerns, and postadoption uncertainty. By aligning these findings with the CFIR constructs, action plans targeting the main challenges were incorporated into the framework, facilitating a structured approach to addressing these issues.
This systematic review identifies key challenges in implementing AI in breast cancer screening, emphasizing the need for consistency, robust evidentiary standards, technological advancements, user trust, ethical frameworks, legal safeguards, and societal benefits. These findings can serve as a blueprint for policy makers, clinicians, and AI developers to collaboratively advance AI adoption in breast cancer screening.
PROSPERO CRD42024553889; https://tinyurl.com/mu4nwcxt.
人工智能（AI）研究显示出在提高全球乳腺钼靶筛查项目的准确性和效率方面具有前景。然而，将其整合到临床工作流程面临诸多挑战，包括意外错误、专业培训需求以及伦理问题。值得注意的是，乳腺癌筛查中人工智能成像的具体框架仍很缺乏。
本研究旨在确定在乳腺筛查项目中实施人工智能相关的挑战，并应用实施研究综合框架（CFIR）来探讨在此背景下人工智能的实用治理框架。
使用“人工智能”“监管”“治理”“乳腺癌”和“筛查”等关键词组合检索三个电子数据库（PubMed、Embase和MEDLINE）。评估人工智能在乳腺癌检测中的应用或讨论在此背景下与人工智能实施相关挑战的原始研究符合纳入综述的条件。研究结果进行叙述性综合，随后直接映射到CFIR的各个构建要素上。
共检索到1240条结果，最终20项原始研究纳入本系统综述。大多数研究（n = 19）聚焦于人工智能增强的乳腺钼靶检查，而1项研究涉及针对乳腺致密女性的人工智能增强超声检查。大多数研究来自美国（n = 5）和英国（n = 4），发表年份从2019年到2023年。论文质量评定为中等至高。确定的关键挑战包括可重复性、证据标准、技术问题、信任问题以及伦理、法律、社会问题和采用后不确定性。通过将这些结果与CFIR构建要素相结合，针对主要挑战的行动计划被纳入框架，促进了应对这些问题的结构化方法。
本系统综述确定了在乳腺癌筛查中实施人工智能的关键挑战，强调了一致性、强有力的证据标准、技术进步、用户信任、伦理框架、法律保障和社会效益的必要性。这些研究结果可为政策制定者、临床医生和人工智能开发者共同推进人工智能在乳腺癌筛查中的应用提供蓝图。
PROSPERO CRD42024553889；https://tinyurl.com/mu4nwcxt 。
