The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human–computer collaboration in clinical practice.

远程医疗的迅猛发展，加上诊断人工智能（AI）的最新发展，必须考虑将基于AI的支持插入新的护理模式中的机会和风险。在这里，我们基于在皮肤癌诊断中基于图像的AI准确性方面的最新成就，来解决不同水平的临床专业知识和多种临床工作流程对基于AI的支持的各种表示形式的影响。我们发现，高质量的基于AI的临床决策支持可提高诊断准确性，优于单独使用AI或医师的诊断准确性，并且经验最少的临床医生将从基于AI的支持中获得最大收益。我们还发现，在移动技术环境中，基于AI的多类概率优于基于AI的基于内容的图像检索（CBIR）表示，基于AI的支持可用于模拟第二意见和远程医疗分类。除了在非专家临床医生手中证明与高质量AI相关的潜在好处外，我们还发现错误的AI可能会误导包括专家在内的整个临床医生。最后，我们证明了从AI类激活图获得的见解可以为人类诊断提供参考。我们的方法和发现共同为基于图像的诊断领域的未来研究提供了框架，以改善临床实践中的人机协作。我们发现错误的AI可能会误导包括专家在内的整个临床医生。最后，我们证明了从AI类激活图获得的见解可以为人类诊断提供参考。我们的方法和发现共同为基于图像的诊断领域的未来研究提供了框架，以改善临床实践中的人机协作。我们发现错误的AI可能会误导包括专家在内的整个临床医生。最后，我们证明了从AI类激活图获得的见解可以为人类诊断提供参考。总之，我们的方法和发现为将来在整个基于图像的诊断领域中进行研究提供了框架，以改善人机在临床实践中的协作。