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.

将数据驱动的人工智能与临床医生的专业知识相结合，开发出一种可理解的诊断模型，通过人工智能的生物标志和检索到的具有相同诊断模式的病例提供可视化的推理过程，从而提高安全性和准确性，该模型在传染性角膜炎的诊断中取得了良好的性能、可解释性和临床实用性。