Current applications and challenges in large language models for patient care: a systematic review
Abstract Background The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized...
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Nature Portfolio
2025-01-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00717-2 |
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| author | Felix Busch Lena Hoffmann Christopher Rueger Elon HC van Dijk Rawen Kader Esteban Ortiz-Prado Marcus R. Makowski Luca Saba Martin Hadamitzky Jakob Nikolas Kather Daniel Truhn Renato Cuocolo Lisa C. Adams Keno K. Bressem |
| author_facet | Felix Busch Lena Hoffmann Christopher Rueger Elon HC van Dijk Rawen Kader Esteban Ortiz-Prado Marcus R. Makowski Luca Saba Martin Hadamitzky Jakob Nikolas Kather Daniel Truhn Renato Cuocolo Lisa C. Adams Keno K. Bressem |
| author_sort | Felix Busch |
| collection | DOAJ |
| description | Abstract Background The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care. Methods We systematically searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4349 initial records, 89 studies across 29 medical specialties were included. Quality assessment was performed using the Mixed Methods Appraisal Tool 2018. A data-driven convergent synthesis approach was applied for thematic syntheses of LLM applications and limitations using free line-by-line coding in Dedoose. Results We show that most studies investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 of 124 different LLMs examined) and GPT-4 (26.6%, n = 33/124) in answering medical questions, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations include 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations include 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias. Conclusions This review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings. |
| format | Article |
| id | doaj-art-0271bdd1bccf4f8b9a26b51482983e1f |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-0271bdd1bccf4f8b9a26b51482983e1f2025-01-26T12:50:00ZengNature PortfolioCommunications Medicine2730-664X2025-01-015111310.1038/s43856-024-00717-2Current applications and challenges in large language models for patient care: a systematic reviewFelix Busch0Lena Hoffmann1Christopher Rueger2Elon HC van Dijk3Rawen Kader4Esteban Ortiz-Prado5Marcus R. Makowski6Luca Saba7Martin Hadamitzky8Jakob Nikolas Kather9Daniel Truhn10Renato Cuocolo11Lisa C. Adams12Keno K. Bressem13School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of MunichDepartment of Neuroradiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu BerlinDepartment of Neuroradiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu BerlinDepartment of Ophthalmology, Leiden University Medical CenterDivision of Surgery and Interventional Sciences, University College LondonOne Health Research Group, Faculty of Health Science, Universidad de Las AméricasSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of MunichDepartment of Radiology, Azienda Ospedaliero Universitaria (A.O.U.)School of Medicine and Health, Institute for Cardiovascular Radiology and Nuclear Medicine, German Heart Center Munich, TUM University Hospital, Technical University of MunichDepartment of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University HospitalDepartment of Diagnostic and Interventional Radiology, University Hospital AachenDepartment of Medicine, Surgery and Dentistry, University of SalernoSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of MunichSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of MunichAbstract Background The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care. Methods We systematically searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4349 initial records, 89 studies across 29 medical specialties were included. Quality assessment was performed using the Mixed Methods Appraisal Tool 2018. A data-driven convergent synthesis approach was applied for thematic syntheses of LLM applications and limitations using free line-by-line coding in Dedoose. Results We show that most studies investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 of 124 different LLMs examined) and GPT-4 (26.6%, n = 33/124) in answering medical questions, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations include 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations include 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias. Conclusions This review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings.https://doi.org/10.1038/s43856-024-00717-2 |
| spellingShingle | Felix Busch Lena Hoffmann Christopher Rueger Elon HC van Dijk Rawen Kader Esteban Ortiz-Prado Marcus R. Makowski Luca Saba Martin Hadamitzky Jakob Nikolas Kather Daniel Truhn Renato Cuocolo Lisa C. Adams Keno K. Bressem Current applications and challenges in large language models for patient care: a systematic review Communications Medicine |
| title | Current applications and challenges in large language models for patient care: a systematic review |
| title_full | Current applications and challenges in large language models for patient care: a systematic review |
| title_fullStr | Current applications and challenges in large language models for patient care: a systematic review |
| title_full_unstemmed | Current applications and challenges in large language models for patient care: a systematic review |
| title_short | Current applications and challenges in large language models for patient care: a systematic review |
| title_sort | current applications and challenges in large language models for patient care a systematic review |
| url | https://doi.org/10.1038/s43856-024-00717-2 |
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