MAGIS: Evidence-Based Multi-Agent Reasoning for Interpretable Strabismus Clinical Decision-Making
2026-06-08 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed MAGIS, a new system to diagnose strabismus, an eye disorder, more accurately and understandably. Unlike older methods that give answers without explanations, MAGIS uses a step-by-step reasoning process based on both images and medical rules to check its guesses and fix mistakes. This approach helped MAGIS perform better in tests and produce clearer, more reliable reports. Overall, the authors show that combining visual evidence with expert rules improves diagnosis and trustworthiness.
StrabismusDeep LearningVision-Language ModelsDiagnostic ReasoningEvidence-Based DiagnosisHypothesis VerificationMedical ImagingExplainable AIClinical Decision Support
Authors
Xikai Tang, Yifan Wang, Jiafan Zhuang, Li Luo, Jinming Guo, Xiaoling Xie, Jiacheng Liu, Peiwei Wei, Lihao Zhong, Xiaoli Kang, Jie Cen, Guangqiang Yin, Kunliang Qiu, Ce Zheng, Zhun Fan
Abstract
Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep learning methods mainly provide diagnostic predictions without transparent reasoning, while recent large vision-language models (LVLMs), although promising for joint image understanding and report generation, remain highly prone to hallucination in this evidence-sensitive and rule-driven medical task. To address these challenges, we propose MAGIS, an evidence-based Multi-AGent reasoning for Interpretable Strabismus diagnosis framework. MAGIS transforms black-box end-to-end generation into a structured diagnostic process consisting of candidate hypothesis generation, dual-evidence constrained context, evidence-based corrective verification, and report generation. Specifically, we introduce a Dual-Evidence Constrained Context (DECC) mechanism that jointly organizes visual evidence from the photograph of the nine cardinal positions of gaze and evidence-based clinical diagnostic rules into a constrained context for reliable diagnostic reasoning. We further develop an Evidence-Based Corrective Verification (EBCV) mechanism that verifies whether the current diagnostic hypothesis is supported by visual evidence, heatmap-based visual cues, and evidence-based clinical diagnostic rules. Hypothesis refinement is triggered when inconsistency is detected. Experiments on a fine-grained strabismus benchmark demonstrate that MAGIS not only significantly outperforms other state-of-the-art diagnostic systems, improving the weighted F1 score from 72.0% to 91.3%, but also substantially improves the clinical reliability (consistency, alignment, and completeness) of generated diagnostic reports. These results demonstrate that MAGIS provides an effective solution for building accurate, evidence-based, and clinically interpretable strabismus diagnosis systems.