COCAgent: Chain-of-Causality Multi-Agent Reasoning for Evidence-Based Medical Decision Making

Strabismus subtype diagnosis requires integrating ocular alignment across gaze positions, deviation patterns, and clinical criteria, yet existing deep learning and vision-language models learn direct mappings from images to predictions, with reasoning that remains implicit and explanations weakly grounded in clinical evidence. We propose COCAgent (Chain-Of-Causality Agent), an multi-agent framework for evidence-based strabismus subtype diagnosis. COCAgent links multimodal ophthalmic evidence through a Chain of Causality: it constructs subtype-specific causal chains, verifies candidate diagnoses against structured clinical criteria, and generates the final diagnostic report and subtype prediction, while a verification mechanism filters out unsupported or inconsistent reasoning. Such causality-grounded diagnosis requires data that expose causal structure, which no existing strabismus dataset provides. We therefore further construct StrabBench, a clinical benchmark comprising the photograph of the nine cardinal positions of gaze, subtype annotations, doctor-in-the-loop diagnostic reports, and task-specific causal graphs with node-level analysis. On StrabBench, COCAgent achieves 93.3% weighted recall, 96.1% BERTScore, and 83.4% CosSim, improving both subtype prediction and report quality over existing approaches.