Will Large Language Models Transform Clinical Prediction?

TL;DR

Large language models have potential to enhance clinical prediction by processing complex EHR data, but face significant methodological, validation, infrastructural, and regulatory challenges that need addressing for effective clinical integration.

Contribution

This paper evaluates the potential and challenges of using large language models to improve clinical prediction models with longitudinal EHR data.

Findings

LLMs can handle multimodal and longitudinal EHR data.

They support multi-outcome predictions for various health conditions.

Significant methodological and infrastructural challenges remain.

Abstract

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.