A roadmap for medical large language models: a review of foundations, applications, and challenges
Abstract
Medical large language models (Med-LLMs) have shown considerable promise across a broad range of clinical tasks, including decision support, medical documentation, patient communication, multimodal analysis, and telemedicine. Their rapid development has generated growing interest in how large language models (LLMs) may support healthcare practice, while also raising important questions about reliability, clinical validity, and safe deployment. This review provides a structured overview of recent progress in Med-LLMs by examining their major application areas, key challenges, and emerging future directions. Current evidence shows that the clinical usefulness of Med-LLMs cannot be judged by model performance alone. Their value in practice depends on whether they are supported by reliable evidence, remain consistent with current medical knowledge, and can be integrated into clinical workflows. Important challenges remain in evaluation, safety, knowledge updating, and real-world deployment. These issues reflect a gap between performance in controlled settings and clinical practice. Future progress will require stronger clinical validation, better alignment with medical practice, and more careful deployment across different settings. The clinical impact of Med-LLMs will depend on whether they can be used as reliable tools in clinical care.
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