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。

整合专业知识与可解释性数据驱动智能实现传染性角膜炎的协作临床诊断

Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by  Integrating Expert Knowledge and Interpretable Data-driven Intelligence

Infectious keratitis is the most common entities of corneal diseases, in
which pathogen grows in the cornea leading to inflammation and destruction of
the corneal tissues. Infectious keratitis is a medical emergency, for which a
rapid and accurate diagnosis is needed for speedy initiation of prompt and
precise treatment to halt the disease progress and to limit the extent of
corneal damage; otherwise it may develop sight-threatening and even
eye-globe-threatening condition. In this paper, we propose a sequential-level
deep learning model to effectively discriminate the distinction and subtlety of
infectious corneal disease via the classification of clinical images. In this
approach, we devise an appropriate mechanism to preserve the spatial structures
of clinical images and disentangle the informative features for clinical image
classification of infectious keratitis. In competition with 421
ophthalmologists, the performance of the proposed sequential-level deep model
achieved 80.00% diagnostic accuracy, far better than the 49.27% diagnostic
accuracy achieved by ophthalmologists over 120 test images.

本文提出了一种用于临床图像分类的序列级深度学习模型，以有效识别感染性角膜病的不同和细微之处。该模型的表现比样本中的 421 名眼科医生实现的 49.27% 的诊断准确率高得多，达到了 80.00% 的准确性。