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Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.

Guillermo Lopez-Garcia¹, Dongfang Xu², Michael Luu³

¹Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA, Guillermo.LopezGarcia@cshs.org.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) can now automatically score physician risk communication in prostate cancer care. This AI-driven approach offers a scalable solution for evaluating patient-doctor discussions.

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Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Effective risk communication is crucial for shared decision-making in prostate cancer treatment.
Current manual evaluation of physician communication is time-consuming and not scalable.
Automating this assessment can improve the quality of patient-physician interactions.

Purpose of the Study:

To develop and evaluate a framework using large language models (LLMs) for automated scoring of risk communication quality.
To assess the performance of LLMs in evaluating physician communication of key concepts related to prostate cancer shared decision-making.
To establish a scalable method for analyzing physician-patient communication in oncology.

Main Methods:

A rubric-based framework was developed to score physician communication on five key concepts (prognosis, life expectancy, erectile dysfunction, incontinence, urinary symptoms).
Transcripts from 20 clinical visits were annotated, with 487 sentences scored from 0 to 5 based on risk communication quality.
The task was modeled as multiclass classification, evaluating finetuned transformer baselines and GPT-4o with rubric-based and chain-of-thought (CoT) prompting, including few-shot learning.

Main Results:

The best performing approach combined rubric-based CoT prompting with few-shot learning.
This method achieved micro-averaged F1 scores between 85.0% and 92.0% across different communication domains.
The AI-driven approach outperformed supervised baselines and matched human inter-annotator agreement.

Conclusions:

LLMs provide a scalable and accurate method for evaluating physician risk communication in prostate cancer consultations.
This AI-driven framework can enhance the quality of shared decision-making by providing objective feedback on communication.
The findings support the broader application of AI for assessing physician-patient communication in healthcare.