TRUSTWORTHY 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 models for medical image analysis, such as Convolutional Neural Networks (CNNs), segmentation networks based on U-net, Vision Transformers, and foundation models, and explores their use in clinical decision support. It also examines the fundamental principles of trustworthy AI, including explainability, robustness, fairness, privacy preservation, reliability, transparency and regulatory governance. The review summarizes the current state of the art for the strengths and limitations of current AI models and highlights some of the challenges that hamper the use of these models in the clinical workflow, such as limited data availability, domain shifts, algorithmic bias, computational complexity, and integration into the clinical workflow. New concepts like multimodal foundation models, federated learning and privacy-preserving AI are also explored as potential advancements in the safe, scalable and clinically relevant use of AI in medical imaging. The review highlights the significant progress medical image analysis has made using deep learning techniques but emphasizes that the effective translation of these technologies into clinical practice requires both technical advancement and adherence to trustworthy AI principles and effective governance models. To ensure these AI systems are effective for patient care and to aid in evidence-based medical decision making, development needs continued cooperation between researchers, clinicians, healthcare institutions and regulatory bodies, with the aim of creating reliable, transparent and clinically acceptable AI systems.