We examined the effects of predictive AI deployment on the immediate performance and learning of medical novices. In two pre-registered field experiments, we varied whether AI input was provided during the training or practice of lung cancer diagnoses, or both. Our results show that different AI deployments have distinct implications for human professionals. AI input during training or practice independently improves individuals' diagnostic accuracy, whereas deployment across both phases yields gains that exceed either approach alone. Furthermore, AI input in both training and earlier practice can improve the accuracy of individuals' subsequent independent diagnoses. Beyond individual accuracy, AI deployment affects the diversity of errors across individuals, with consequences for the accuracy of group decisions (e.g. when getting a second or third opinion on a diagnosis).

翻译：本研究考察了预测性人工智能部署对医学新手即时表现与学习效果的影响。通过两项预先注册的实地实验，我们控制了人工智能辅助在肺癌诊断培训阶段、实践阶段或双阶段的介入条件。实验结果表明，不同的人工智能部署模式对专业人员产生差异化影响：在培训或实践阶段单独引入人工智能辅助均能提升个体诊断准确率，而双阶段部署产生的增益效应超越任一单独模式。值得注意的是，在培训和早期实践阶段同时引入人工智能辅助，还能提升个体后续独立诊断的准确率。除个体准确性外，人工智能部署会影响个体间错误分布的多样性，这对群体决策准确性（例如获取第二或第三诊疗意见时）具有重要影响。