Artificial Intelligence for Medical Imaging Diagnosis: From Accuracy to Clinical Reliability through Multimodal Fusion, Validation, and Regulatory Perspectives
DOI:
https://doi.org/10.70882/josrar.2026.v3i4.230Keywords:
Artificial Intelligence, Medical Imaging, Multimodal Fusion, Clinical Validation, Explainable AI, Regulatory ComplianceAbstract
Artificial intelligence (AI)-based medical imaging diagnosis has demonstrated remarkable performance across multiple clinical domains, with deep learning models frequently reporting diagnostic accuracy, sensitivity, and specificity exceeding 90% under controlled experimental conditions. However, translating these results into clinically reliable, regulatory-compliant systems remains a critical challenge. As a narrative survey rather than an original benchmark study, this paper reports no new experimental results; instead, it introduces a modality-aware analytical framework organizing the existing literature across four dimensions: imaging modality, data provenance, validation maturity, and model architecture. Using this taxonomy, the survey synthesizes unimodal and multimodal fusion approaches spanning radiology (CT, MRI, X-ray), pathology (whole-slide images), ophthalmology (fundus photography, OCT), and multi-source fusion combining imaging with electronic health records (EHR) and genomic data. The synthesis indicates that high reported accuracy is strongly contingent on data characteristics and evaluation conditions, with many models relying on low-maturity validation lacking evidence of generalization in real-world settings. To address these limitations, an engineering-oriented deployment framework is proposed, integrating modality-driven model selection, structured preprocessing pipelines, multi-level clinical validation, computational feasibility assessment, and explainability, together with a clinical deployment readiness model spanning validation maturity, data diversity, interpretability, and regulatory alignment. Key challenges include the single-site generalization gap, algorithmic bias across demographic groups, limited clinical adoption of explainable AI, insufficient alignment with regulatory frameworks including FDA 510(k), De Novo, and EU MDR/IVDR pathways, and a continuing need for prospective multicenter validation. Future directions toward federated learning, foundation models, certification-aware design, and multimodal digital biomarker integration are outlined.
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