# LLM Clinical Triage Performance Compared to Physician Expertise

> Recent research on large language models (LLMs) in healthcare settings has raised important questions about their reliability, particularly in clinical triage. This study rigorously evaluates LLMs by...

**Source**: arxiv.org | **Published**: 2026-10-01 | **Type**: research

## Key Facts

- Assess LLM reliability in clinical triage to minimize unnecessary medical interventions.
- Implement findings to refine LLM training for better alignment with physician decision-making.
- Leverage extensive datasets to enhance LLM performance and accuracy in real-world scenarios.
- Monitor healthcare costs associated with LLM recommendations to improve budgeting and resource allocation.
- Foster collaboration between AI developers and healthcare professionals to optimize clinical applications.

## Summary

**Paper:** [Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts](https://arxiv.org/abs/2609.38600)

**Authors:** Abinitha Gourabathina, Haoran Zhang, Yuexing Hao, Walter Gerych, Marzyeh Ghassemi

\## Executive Summary

Recent research on large language models (LLMs) in healthcare settings has raised important questions about their reliability, particularly in clinical triage. This study rigorously evaluates LLMs by comparing their performance to that of practicing physicians. It focuses on how LLMs respond to variations in clinical text that still reflect real-world scenarios.

To conduct this evaluation, the researchers developed a comprehensive benchmark consisting of over 6,000 clinical scenarios, 7,000 annotations from physicians, and 225,000 responses generated by LLMs. This extensive dataset allows for a robust comparison between LLMs and human practitioners.

The research reveals two significant findings. First, LLMs tend to recommend unnecessary medical interventions more frequently than physicians do. This tendency is concerning, as unnecessary care can lead to increased healthcare costs and potential harm to patients. Moreover, under variations in the text—specifically changes that do not alter the fundamental clinical context—this tendency to recommend unnecessary care worsens. This suggests that LLMs may not adapt well to slight modifications in language, which could happen in real clinical communications.

Second, the study notes that LLM recommendations are particularly sensitive to variations in gender and tone within the clinical text. This sensitivity indicates that LLMs may respond inconsistently to factors that should not affect clinical decision-making. In contrast, physicians appear to be more stable in their recommendations despite such variations. 

These findings highlight the need for careful evaluation of LLMs before they are implemented in healthcare settings. The research emphasizes that deployment assessments should be grounded in expert physician behavior to ensure that LLMs do not inadvertently lead to suboptimal patient care. 

The implications for healthcare organizations considering the use of LLMs are significant. Organizations may wish to proceed with caution and ensure thorough validation of these models against established clinical standards. Understanding the limitations of LLMs in handling clinical text variations is essential to deploying these technologies safely and effectively in patient care settings.

\## Academic Abstract

As large language models (LLMs) are increasingly used in clinical settings, it is critical to evaluate their reliability under realistic variation in clinical text. We study this question in clinical triage, comparing LLMs to practicing physicians under text perturbations that preserve the underlying clinical setting. We introduce a benchmark of over 6,000 clinical scenarios, 7,000 physician annotations, and 225,000 model responses. Using this benchmark, we make two key observations. First, LLMs are more likely than physicians to recommend unnecessary care at baseline, and this tendency increases under perturbed inputs. Further, we find that LLM recommendations are more sensitive to gender and tone perturbations than human recommendations. Together, these results demonstrate that LLMs can vary under clinically irrelevant textual changes, highlighting the need for deployment-oriented evaluations grounded in expert physician behavior.

## Frequently Asked Questions

**What business problems does this research address?**

This research highlights concerns regarding the reliability of large language models (LLMs) in clinical triage, particularly their tendency to recommend unnecessary medical interventions, which could lead to increased healthcare costs and potential harm to patients.

**Which industries could benefit most from the findings of this research?**

The healthcare industry could benefit significantly from this research, as it directly addresses the performance of LLMs in clinical settings and their implications for patient care and cost management.

**What are the practical implementation considerations for businesses looking to use LLMs in clinical triage?**

Businesses must consider the reliability of LLMs in providing accurate triage recommendations, particularly in avoiding unnecessary medical interventions, as highlighted by the findings of this study.

**What resources or expertise are needed for effectively implementing LLMs in healthcare settings?**

Organizations would need expertise in clinical practices to evaluate LLM recommendations effectively, as well as resources to develop and maintain robust benchmarks for assessing LLM performance against human practitioners.

**What competitive advantages could arise from utilizing the findings of this research in healthcare?**

By leveraging insights from this research, healthcare organizations could improve patient outcomes and reduce unnecessary costs, potentially gaining a competitive edge in the market through enhanced triage processes and more efficient resource allocation.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/llm-clinical-triage-performance-compared-to-physician-expertise)
- [Original source](https://arxiv.org/abs/2609.38600)

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Source: Welcome.AI | https://welcome.ai/content/llm-clinical-triage-performance-compared-to-physician-expertise