aceTRUSTWORTHY ARTIFICIAL INTELLIGENCE FOR MEDICAL IMAGE ANALYSIS: A COMPREHENSIVE REVIEW OF ROBUST DEEP LEARNING TECHNIQUES FOR CLINICAL DECISION SUPPORT
Medical image analysis has been revolutionized by the advent of artificial intelligence, which has made automated disease detection, segmentation and diagnosis possible across a range of imaging modalities, thus supporting clinical decisions. However, the increasing complexity of deep learning models has raised concerns regarding their reliability, interpretability, fairness, privacy, and regulatory compliance, highlighting the need for trustworthy AI in healthcare. The review focuses on recent breakthroughs in…
Deep learning has made automated disease detection, segmentation and diagnosis possible across imaging modalities, supporting clinical decisions.
Limited data, domain shifts, algorithmic bias, computational complexity and workflow integration hinder clinical use of medical imaging AI, and reliability, fairness and privacy concerns persist.
The source is a narrative summary of the field and reports no specific clinical outcomes; it frames federated learning, privacy-preserving AI and multimodal foundation models as potential advancements rather than proven ones.
Evidence
- Peer-reviewedMagna Scientia Advanced Research and Reviews2026-10-14
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Truvace Impact Record TRV-2026-1481, v1: “TRUSTWORTHY ARTIFICIAL INTELLIGENCE FOR MEDICAL IMAGE ANALYSIS: A COMPREHENSIVE REVIEW OF ROBUST DEEP LEARNING TECHNIQUES FOR CLINICAL DECISION SUPPORT.” Truvace, 2026-10-15. /record/TRV-2026-1481 (accessed at citation time). sha256 339ae68ef9d03f1b…
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