Current applications and challenges in large language models for patient care: a systematic review
TUM Klinikum · Technical University of Munich · +15 more institutions
Abstract
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.
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.
Citation impact
- FWCI
- 94.26
- Percentile
- 100%
- References
- 134
Authors
14- FBFelix BuschCorresponding
TUM Klinikum, Technical University of Munich
- LHLena Hoffmann
Humboldt-Universität zu Berlin, Freie Universität Berlin, Charité - Universitätsmedizin Berlin
- CRChristopher Rueger
Humboldt-Universität zu Berlin, Freie Universität Berlin, Charité - Universitätsmedizin Berlin
- EHElon H. C. van Dijk
Leiden University Medical Center, Sir Charles Gairdner Hospital
- RKRawen Kader
University College London
Topics & keywords
- Readability
- Automatic summarization
- Medicine
- Medical education
- Computer science
- Artificial intelligence
- Quality Education