# Will Large Language Models Transform Clinical Prediction?

arXiv: https://arxiv.org/abs/2505.18246 · HTML: https://mlsanity.com/paper/2505.18246/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks](https://mlsanity.com/paper/2111.08585/) (2021)
- [Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review](https://mlsanity.com/paper/2010.02809/) (2020)
- [CPLLM: Clinical Prediction with Large Language Models](https://mlsanity.com/paper/2309.11295/) (2023)
- [Federated Learning of Medical Concepts Embedding using BEHRT](https://mlsanity.com/paper/2305.13052/) (2023)
- [Clinical Prediction Models to Predict the Risk of Multiple Binary Outcomes: a comparison of approaches](https://mlsanity.com/paper/2001.07624/) (2020)

## Built on by (most cited first, 3 of 3)

- [LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data](https://mlsanity.com/paper/2606.19509/) (2026)
- [Large Language Models as Unified Multimodal Learners for Clinical Prediction](https://mlsanity.com/paper/2607.15380/) (2026)
- [A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction](https://mlsanity.com/paper/2607.20453/) (2026)

## Abstract (arXiv)

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.