Although data-driven artificial intelligence (AI) in medical image diagnosis has shown impressive performance in silico, the lack of interpretability makes it difficult to incorporate the "black box" into clinicians' workflows. To make the diagnostic patterns learned from data understandable by clinicians, we develop an interpretable model, knowledge-guided diagnosis model (KGDM), that provides a visualized reasoning process containing AI-based biomarkers and retrieved cases that with the same diagnostic patterns. It embraces clinicians' prompts into the interpreted reasoning through human-AI interaction, leading to potentially enhanced safety and more accurate predictions. This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness. The classification performance of KGDM is evaluated on a prospective validation dataset, an external testing dataset, and an publicly available testing dataset. The diagnostic odds ratios (DOR) of the interpreted AI-based biomarkers are effective, ranging from 3.011 to 35.233 and exhibit consistent diagnostic patterns with clinic experience. Moreover, a human-AI collaborative diagnosis test is conducted and the participants with collaboration achieved a performance exceeding that of both humans and AI. By synergistically integrating interpretability and interaction, this study facilitates the convergence of clinicians' expertise and data-driven intelligence. The promotion of inexperienced ophthalmologists with the aid of AI-based biomarkers, as well as increased AI prediction by intervention from experienced ones, demonstrate a promising diagnostic paradigm for infectious keratitis using KGDM, which holds the potential for extension to other diseases where experienced medical practitioners are limited and the safety of AI is concerned.

翻译：尽管数据驱动的人工智能在医学图像诊断中展现了出色的计算机模拟性能，但缺乏可解释性使得"黑箱"难以融入临床医生的工作流程。为使数据驱动的诊断模式能被临床医生理解，我们开发了可解释模型——知识引导诊断模型（KGDM），该模型提供包含AI生物标志物及具有相同诊断模式的检索病例的可视化推理过程。通过人机交互将临床医生的提示整合到可解释推理中，从而增强安全性并提升预测准确性。本研究系统评估了KGDM在感染性角膜炎（IK）诊断中的性能、可解释性及临床实用性（IK是导致角膜盲的主要原因）。我们在前瞻性验证数据集、外部测试数据集及公开测试数据集上评估了KGDM的分类性能。所解释的AI生物标志物的诊断优势比（DOR）效果显著（介于3.011至35.233之间），且与临床经验呈现一致的诊断模式。此外，我们进行了人机协同诊断测试，参与协同诊断者的表现超越单独使用人类或AI的诊断水平。通过可解释性与交互性的协同整合，本研究促进了临床医生专业知识与数据驱动智能的融合。基于AI生物标志物辅助的初级眼科医生能力提升，以及资深医生干预对AI预测准确率的增强，共同证明了KGDM在感染性角膜炎诊断中的潜力，这种诊断范式有望推广至其他缺乏资深医疗从业者且需关注AI安全性的疾病领域。