Large Language Models in Clinical Decision Support: A Critical Review of Progress, Limitations, and the Path Toward Sustainable Healthcare

Large language models such as GPT-4, Med-PaLM 2, and BioMedLM are being increasingly applied to clinical decision support tasks including diagnostic reasoning, clinical documentation, medical question answering, and patient-facing health communication, with several studies reporting performance comparable to trained clinicians on structured benchmarks. However, the current pace of adoption has outrun the critical evaluation of whether these models are genuinely safe, equitable, and sustainable for deployment in real-world healthcare environments. This narrative review argues that the existing literature has focused disproportionately on benchmark performance while giving insufficient attention to the conditions required for these models to function as trustworthy long-term clinical tools. Drawing on published evidence across medical informatics, clinical artificial intelligence, and natural language processing, we synthesise model performance across four clinical task areas and examine documented limitations including factual hallucination, demographic bias, and poor generalisability outside controlled settings that are already present in current deployments. We further identify five structural sustainability challenges that obstruct responsible deployment at scale, and propose a corresponding five-pillar framework alongside a priority research agenda for closing the gaps this review identifies. This review contributes a critically grounded perspective to the growing discourse on artificial intelligence for sustainable healthcare, with direct relevance to Sustainable Development Goal 3 (Good Health and Well-Being) and Sustainable Development Goal 10 (Reduced Inequalities), and offers concrete guidance for developing clinical models that are safe, inclusive, and viable across diverse global health contexts.