Investigating LLMs in Clinical Triage: Promising Capabilities, Persistent Intersectional Biases

• URL: http://arxiv.org/abs/2504.16273v1
• Date: Tue, 22 Apr 2025 21:11:47 GMT
• Title: Investigating LLMs in Clinical Triage: Promising Capabilities, Persistent Intersectional Biases
• Authors: Joseph Lee, Tianqi Shang, Jae Young Baik, Duy Duong-Tran, Shu Yang, Lingyao Li, Li Shen,
• Abstract summary: Large Language Models (LLMs) have shown promise in clinical decision support, yet their application to triage remains underexplored.<n>We systematically investigate the capabilities of LLMs in emergency department triage through two key dimensions.<n>We assess multiple LLM-based approaches, ranging from continued pre-training to in-context learning, as well as machine learning approaches.
• Score: 6.135648377533492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown promise in clinical decision support, yet their application to triage remains underexplored. We systematically investigate the capabilities of LLMs in emergency department triage through two key dimensions: (1) robustness to distribution shifts and missing data, and (2) counterfactual analysis of intersectional biases across sex and race. We assess multiple LLM-based approaches, ranging from continued pre-training to in-context learning, as well as machine learning approaches. Our results indicate that LLMs exhibit superior robustness, and we investigate the key factors contributing to the promising LLM-based approaches. Furthermore, in this setting, we identify gaps in LLM preferences that emerge in particular intersections of sex and race. LLMs generally exhibit sex-based differences, but they are most pronounced in certain racial groups. These findings suggest that LLMs encode demographic preferences that may emerge in specific clinical contexts or particular combinations of characteristics.

Related papers

• How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making? [2.7476176772825904]
  This research investigates the evaluation and mitigation of bias in Large Language Models (LLMs) We introduce a novel Counterfactual Patient Variations (CPV) dataset derived from the JAMA Clinical Challenge. Using this dataset, we built a framework for bias evaluation, employing both Multiple Choice Questions (MCQs) and corresponding explanations.
  arXiv  Detail & Related papers  (2024-10-21T23:14:10Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse.<n>Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration.<n>To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? [60.01746782465275]
  Large Language Models (LLMs) have shown capabilities close to human performance in various analytical tasks. This paper investigates the efficiency and accuracy of LLMs in specialized tasks through a structured user study focusing on Human-LLM partnership.
  arXiv  Detail & Related papers  (2024-10-07T02:30:18Z)
• Hate Personified: Investigating the role of LLMs in content moderation [64.26243779985393]
  For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected.
  arXiv  Detail & Related papers  (2024-10-03T16:43:17Z)
• Direct-Inverse Prompting: Analyzing LLMs' Discriminative Capacity in Self-Improving Generation [15.184067502284007]
  Even the most advanced LLMs experience uncertainty in their outputs, often producing varied results on different runs or when faced with minor changes in input. We propose and analyze three discriminative prompts: direct, inverse, and hybrid. Our insights reveal which discriminative prompt is most promising and when to use it.
  arXiv  Detail & Related papers  (2024-06-27T02:26:47Z)
• D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities of Large Language Models [5.439020425819001]
  Large language models (LLMs) have garnered significant attention and widespread usage due to their impressive performance in various tasks. However, they are not without their own set of challenges, including issues such as hallucinations, factual inconsistencies, and limitations in numerical-quantitative reasoning.
  arXiv  Detail & Related papers  (2024-05-07T10:11:14Z)
• Locally Differentially Private In-Context Learning [8.659575019965152]
  Large pretrained language models (LLMs) have shown surprising In-Context Learning (ICL) ability. This paper proposes a locally differentially private framework of in-context learning (LDP-ICL) Considering the mechanisms of in-context learning in Transformers by gradient descent, we provide an analysis of the trade-off between privacy and utility in such LDP-ICL.
  arXiv  Detail & Related papers  (2024-05-07T06:05:43Z)
• Fairness in Large Language Models: A Taxonomic Survey [2.669847575321326]
  Large Language Models (LLMs) have demonstrated remarkable success across various domains.<n>Despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations.
  arXiv  Detail & Related papers  (2024-03-31T22:22:53Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity [61.54815512469125]
  This survey addresses the crucial issue of factuality in Large Language Models (LLMs) As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
  arXiv  Detail & Related papers  (2023-10-11T14:18:03Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.