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Evaluation over Generalist Large Language Models and Specialised Models for Clinical Risk Prediction.

Zou Lai^1,2, Guodao Zhang³, Winfried Post⁴

¹Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing 314000, China.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show promise for clinical risk prediction but struggle with rare diseases. Hybrid AI models combining LLMs with specialized tools offer improved accuracy for conditions like cholangiocarcinoma.

Keywords:

Clinical risk prediction EHR LLM machine learning

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Area of Science:

Artificial Intelligence in Medicine
Clinical Informatics
Biomedical Data Science

Background:

Large language models (LLMs) demonstrate potential in healthcare.
LLMs face accuracy challenges in disease risk prediction, particularly for rare conditions.
Evaluating LLM performance against established clinical prediction models is crucial.

Purpose of the Study:

To assess the accuracy of standalone and collaborative LLMs (ChatGPT, DeepSeek) for disease risk prediction.
To compare LLM performance against task-specific models for rare (cholangiocarcinoma) and common (myocardial infarction) diseases.
To explore the potential of hybrid AI architectures in clinical settings.

Main Methods:

Comparative analysis of standalone LLMs (ChatGPT, DeepSeek) and task-specific models.
Evaluation of a collaborative prediction framework integrating LLMs and specialized models.
Disease risk prediction tasks for cholangiocarcinoma and myocardial infarction.

Main Results:

Standalone LLMs underperformed specialized models in disease risk prediction.
Collaborative prediction frameworks significantly improved the performance of LLMs.
Hybrid approaches demonstrated enhanced accuracy, especially for rare disease prediction.

Conclusions:

LLMs alone have limitations for clinical disease risk prediction, especially for rare conditions.
Hybrid AI architectures combining LLMs with specialized models offer a promising direction for clinical AI.
Collaborative prediction strategies can leverage the complementary strengths of different AI approaches for improved healthcare outcomes.