The future landscape of large language models in medicine
By J. Clusmann, F. Kolbinger, H. Muti, Zunamys I. Carrero, Jan-Niklas Eckardt, Narmin Ghaffari Laleh, C. Löffler, Sophie-Caroline Schwarzkopf, Michaela Unger, G. Veldhuizen and 2 more. Communications Medicine, 2023
969 citations recorded by the source corpus.
Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public attention after OpenAI’s ChatGPT was made publicly available in November 2022. LLMs can often answer questions, summarize, paraphrase and translate text on a level that is nearly indistinguishable from human capabilities. The possibility to actively interact with models like ChatGPT makes LLMs attractive tools in various fields, including medicine. While these models have the potential to democratize medical knowledge and facilitate access to healthcare, they could equally distribute misinformation and exacerbate scientific misconduct due to a lack of accountability and transparency. In this article, we provide a systematic and comprehensive overview of the potentials and limitations of LLMs in clinical practice, medical research and medical education.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/282c568302701bc163d454702eae10e43ca784a3
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In Health Informatics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, neutral): “In health care, LLMs have shown remarkable potential, primarily through their ability to process and analyze textual content [2,3].”
- Safe RLHF: Safe Reinforcement Learning from Human Feedback (2023, neutral): “Such unpredictability in behavior can be especially perilous in sensitive domains like medicine (Thirunavukarasu et al., 2023; Clusmann et al., 2023), where model-generated misinformation could have severe repercussions.”
- Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, background): “Also, LLMs have the potential to serve as a decision support tool by analysing patient charts and flagging discrepancies or gaps in care [45].”
- HealthBench: Evaluating Large Language Models Towards Improved Human Health (2025, neutral): “With the success of scaling language models, there has been increased interest in their application in healthcare (Clusmann et al., 2023; Thirunavukarasu et al., 2023; Lee et al., 2023; Liu et al., 2023; Moor et al., 2023; Singhal et al., 2023; Omiye et al., 2024; Zakka et al., 2024; Nori et al.,…”
- The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs) (2024, background): “While we recognize that these issues are affected by the use of LLMs in significant ways 6,35,36 these, challenges are not specific to health-related applications.”
- “Small” Large Language Models in the Hospital: Evaluation Study on Real-World Data in a Resource-Constrained Setting (2025, unknown): “This versatility and the observed high performance in standardized benchmarks prompted advocates across academia [4] and industry [5] to call for broader adoption of LLMs into medical informatics systems, for example, for decision support or assistance in operational and administrative tasks [6].”