Deep learning and generative AI for medical imaging and clinical decision support systems: a structured critical review
Abstract
Deep learning (DL) and generative artificial intelligence (generative AI) are changing how medical data are analysed and used at the point of care. Our evidence base comprises 80 sources 37 screened studies and 43 landmark primary studies, architectural papers and clinical-AI reporting standards published between 2014 and 2026, across two related domains: medical image analysis and clinical decision support systems (CDSS). We trace the evolution from convolutional neural networks (CNNs) to U-Net and encoder-decoder networks, then to Vision Transformers (ViTs), generative adversarial networks (GANs), diffusion models, large language models (LLMs) and retrieval-augmented generation (RAG). Each model family is compared across nine dimensions: input modality, task, data requirements, validation level, interpretability, failure modes, clinical readiness, regulatory considerations and human-oversight need. Supervised DL reaches clinically useful performance across CT, MRI and pathology on well-scoped detection, segmentation and classification tasks, though results are task-, dataset- and site-dependent and prospective evidence is limited. To address data scarcity, generative models can produce synthetic images or cross-modality translations, but may amplify hidden dataset biases and generate anatomically incorrect images. LLM-based CDSS show promise for guideline-concordant reasoning and medication-safety checks, yet still face hallucination, calibration and regulatory…
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