Large Language Models (LLMs) are advanced artificial intelligence systems that leverage deep learning and natural language processing (NLP) techniques, trained on extensive datasets to effectively interpret and generate human language. Prominent examples include OpenAI’s GPT series and BERT. These models are characterized by their large scale, often containing billions of parameters, and their ability to understand the context of words, which is crucial for accurate language understanding and production. The Transformer architecture, with its self-attention mechanism, is fundamental to their capability to weigh the relative importance of different words, enhancing contextual understanding.
In healthcare, the integration of LLMs has garnered significant interest due to their potential to improve diagnostic accuracy, personalize treatment, and enhance patient care efficiency. They contribute across various domains, including medical information retrieval, patient data analysis, and clinical decision support systems. For example, LLMs can assist in diagnosing diseases by processing medical literature, develop personalized treatment plans by evaluating extensive patient data, and propose potential treatment options through the analysis of medical research articles. Their utility extends to supporting epidemiological analysis and public health strategy formulation, as demonstrated during the COVID-19 pandemic, and they can streamline operations to reduce costs across the healthcare sector. Furthermore, LLMs can aid in creating and organizing clinical notes, extracting relevant information from electronic health records (EHRs), empowering chatbots and virtual assistants, and predicting disease outbreaks and patient outcomes.
The field of LLMs in healthcare is experiencing rapid expansion, with a notable surge in research and publication activities over the past three years, specifically from 2021 to 2024. Over 500 articles were analyzed in a recent study, reflecting concerted efforts to harness the potential of artificial intelligence and machine learning in this domain. Research has been largely concentrated in the United States, Germany, and the United Kingdom, identified as the top contributing countries. The USA leads in both the number of publications and citations within this field. Significant growth in publications has been observed in areas such as neural network applications in diagnostic imaging, natural language processing for clinical documentation, and patient data analysis across various medical specialties, including general internal medicine, radiology, medical informatics, healthcare services, surgery, oncology, ophthalmology, neurology, orthopedics, and psychiatry. Keyword trend analysis highlights emerging sub-themes such as clinical research, artificial intelligence, ChatGPT, education, natural language processing, clinical management, virtual reality, and chatbots, indicating a shift towards addressing broader implications of LLM applications.
Despite the transformative potential, the widespread integration of LLMs in healthcare faces several key challenges. These include critical issues related to data privacy, ethical concerns, and compliance within healthcare settings. Technical and computational hurdles also persist, such as the high demands for training and deploying LLMs, the need for diversity in training datasets to ensure generalizability across populations, and challenges with interoperability and standardization of AI systems. Furthermore, the “black-box” nature of many advanced AI models raises concerns about transparency and interpretability, which are vital for building trust among clinicians and patients. Overcoming these challenges is paramount for fully realizing the potential of LLMs. The successful integration of LLMs into healthcare demands continued research, development, and extensive interdisciplinary collaboration among technologists, clinicians, and policymakers. Future efforts must prioritize translational research to bridge the gap between theoretical innovations and practical, clinically applicable solutions, ultimately aiming to revolutionize healthcare delivery globally.
Reference:
Gencer, G., & Gencer, K. (2025). Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends. Journal of Multidisciplinary Healthcare, 18, 223–238.
