Multimodal Clinical Decision Support Using Vision-Language Models for Robust Gastrointestinal Disease Diagnosis and Medical Report Generation
Keywords:
clinical decision support; vision-language models; gastrointestinal disease diagnosis; medical report generation; multimodal learning; robustness; algorithmic governanceAbstract
Multimodal clinical decision support systems are emerging as a promising response to the fragmented information environment of gastrointestinal diagnosis. Endoscopic findings, patient history, laboratory data, and prior procedural notes must be interpreted together, yet most existing systems address only isolated tasks such as polyp detection or text classification. This paper examines the design of vision-language models for robust gastrointestinal disease diagnosis and medical report generation from a systems perspective. The analysis emphasizes structural trade-offs in multimodal integration, clinical reasoning, perceptual verification, language generation, and deployment infrastructure. A central argument is that diagnostic safety depends less on benchmark accuracy alone and more on the ability of the system to maintain calibrated behavior under input variability, documentation heterogeneity, and device-induced visual drift. The paper discusses architectural choices including modular encoders, retrieval-augmented clinical reasoning, structured report generation, and asynchronous deployment. It further addresses fairness, bias, governance, and sustainability as first-order design constraints rather than post hoc considerations. The proposed perspective connects visual segmentation, large language modeling, and institutional governance into a coherent framework for responsible clinical translation. Future directions include longitudinal multimodal learning, federated governance mechanisms, and prospective clinical evaluation that accounts for report quality, workflow integration, and patient safety.
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