Large language models for intelligent healthcare and medicine: A systematic survey
Abstract
Against the backdrop of healthcare digitalization, intelligent medical systems, and the rapid expansion of multi-source medical data, large language models are reshaping medical artificial intelligence from a traditional task-driven paradigm into a generative, interactive, and multimodal intelligence paradigm. This paper systematically reviews recent progress in large language models and multimodal large language models for intelligent healthcare and medicine, with emphasis on technological evolution, model architectures, training data, evaluation systems, medical applications, and trustworthy deployment. The review first traces the development of medical artificial intelligence from supervised learning and pre-trained language models to large language models and multimodal large language models. It then summarizes the backbone architectures and domain adaptation methods of medical large language models, as well as modality encoding, alignment mechanisms, and task interfaces in multimodal systems. In addition, we discuss medical data resources, fine-tuning strategies, evaluation frameworks, and their practical value in diagnosis augmentation, clinical document generation, medical education, mental health services, and specialized clinical scenarios. Although existing studies have demonstrated substantial potential, medical large language models still face critical challenges, including hallucinations, insufficient factual reliability, privacy and data security risks, fairness and bias concerns, limited interpretability, and barriers to clinical deployment. Finally, we outline promising future directions, including retrieval-augmented generation, multimodal fusion, privacy-preserving training, human-machine collaboration, and general medical artificial intelligence. This survey aims to provide a comprehensive reference for trustworthy research, engineering implementation, and intelligent healthcare applications of medical large language models.
Document Type: Invited review
Cited as: Wu, C., Zhu, Y. Large language models for intelligent healthcare and medicine: A systematic survey. Advanced IntelliEngineering, 2026, 1(1): 21-44. https://doi.org/10.46690/aie.2026.01.03
DOI:
https://doi.org/10.46690/aie.2026.01.03Keywords:
Large language models, medical artificial intelligence, intelligent healthcare, medical engineering, intelligent medical systemsReferences
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