Will Large Language Models Transform Clinical Prediction?
This commentary addresses the problem of integrating LLMs into clinical prediction for healthcare, but it is incremental as it reviews existing challenges without presenting new solutions.
The paper evaluates the potential of large language models (LLMs) to improve clinical prediction models for diagnostic and prognostic tasks by processing longitudinal electronic health record data, finding promise in handling multimodal data and supporting multi-outcome predictions but highlighting challenges like poor calibration, limited validation, and bias.
Objective: Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to process longitudinal electronic health record (EHR) data. Findings: LLMs show promise in handling multimodal and longitudinal EHR data and can support multi-outcome predictions for diverse health conditions. However, methodological, validation, infrastructural, and regulatory chal- lenges remain. These include inadequate methods for time-to-event modelling, poor calibration of predictions, limited external validation, and bias affecting underrepresented groups. High infrastructure costs and the absence of clear regulatory frameworks further prevent adoption. Implications: Further work and interdisciplinary collaboration are needed to support equitable and effective integra- tion into the clinical prediction. Developing temporally aware, fair, and explainable models should be a priority focus for transforming clinical prediction workflow.