Deep learning and generative AI for medical imaging and clinical decision support systems: a structured critical review
TL;DR
It is argued that LLM-based CDSS are not supported for routine autonomous use and require clinician supervision, and set out a research agenda centred on validation, governance and human-in-the-loop deployment.
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 uncertainty. For each technology we provide a deployment-readiness map, review the reporting standards needed for credible clinical evaluation (CONSORT-AI, SPIRIT-AI, TRIPOD + AI, CLAIM, DECIDE-AI, STARD-AI, PROBAST + AI, FUTURE-AI) and set out a research agenda centred on validation, governance and human-in-the-loop deployment. Unlike prior surveys, which treat imaging AI or clinical LLMs separately, we assess both, add an explicit evidence-quality appraisal and link each technology to the reporting standards. On current evidence, we argue that LLM-based CDSS are not supported for routine autonomous use and require clinician supervision. This is not a PRISMA-style systematic review but a structured critical narrative review built on a curated, transparently reported evidence base.