如何让医疗AI更透明可信？专家给出技术临床监管三方面建议。
Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations
- 组建跨领域专家小组评估AI在临床决策中的表现与交互方式。
- 实证发现医生使用AI时存在认知偏差，需针对性培训提升信任度。
- 强调可解释性设计、人员培训与监管协同，保障AI医疗设备安全落地。
人工智能（AI）和机器学习（ML）在医疗领域的应用日益增长，尤其作为临床决策支持系统辅助医护人员。然而，许多模型因复杂性被称为“黑箱”，难以理解其预测逻辑，这引发了在临床环境中安全集成的担忧。本文基于英国药品及医疗器械管理局（MHRA）召集的专家工作组的见解与建议展开讨论。工作组由医疗从业者、监管机构与数据科学家组成，重点评估不同AI算法在临床决策场景下的输出表现，并结合一项试点研究，考察医生在诊断过程中与AI系统的互动行为。研究指出，确保医疗AI设备在临床环境中的安全性与可信度至关重要。充分的多方培训是应对潜在问题的关键，论文进一步提出了推动AI系统在医疗中安全应用的具体建议。
原文摘要 · Abstract (English)
There is a growing demand for the use of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, particularly as clinical decision support systems to assist medical professionals. However, the complexity of many of these models, often referred to as black box models, raises concerns about their safe integration into clinical settings as it is difficult to understand how they arrived at their predictions. This paper discusses insights and recommendations derived from an expert working group convened by the UK Medicine and Healthcare products Regulatory Agency (MHRA). The group consisted of healthcare professionals, regulators, and data scientists, with a primary focus on evaluating the outputs from different AI algorithms in clinical decision-making contexts. Additionally, the group evaluated findings from a pilot study investigating clinicians' behaviour and interaction with AI methods during clinical diagnosis. Incorporating AI methods is crucial for ensuring the safety and trustworthiness of medical AI devices in clinical settings. Adequate training for stakeholders is essential to address potential issues, and further insights and recommendations for safely adopting AI systems in healthcare settings are provided.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。