CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions

• URL: http://arxiv.org/abs/2406.09923v1
• Date: Fri, 14 Jun 2024 11:10:17 GMT
• Title: CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions
• Authors: Mingyu Derek Ma, Chenchen Ye, Yu Yan, Xiaoxuan Wang, Peipei Ping, Timothy S Chang, Wei Wang,
• Abstract summary: We introduce CliBench, a novel benchmark developed from the MIMIC IV dataset. This benchmark offers a comprehensive and realistic assessment of LLMs' capabilities in clinical diagnosis. We conduct a zero-shot evaluation of leading LLMs to assess their proficiency in clinical decision-making.
• Score: 16.310913127940857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency and accessibility of medical care. While LLMs have shown some promise in the medical domain, their application in clinical diagnosis remains underexplored, especially in real-world clinical practice, where highly sophisticated, patient-specific decisions need to be made. Current evaluations of LLMs in this field are often narrow in scope, focusing on specific diseases or specialties and employing simplified diagnostic tasks. To bridge this gap, we introduce CliBench, a novel benchmark developed from the MIMIC IV dataset, offering a comprehensive and realistic assessment of LLMs' capabilities in clinical diagnosis. This benchmark not only covers diagnoses from a diverse range of medical cases across various specialties but also incorporates tasks of clinical significance: treatment procedure identification, lab test ordering and medication prescriptions. Supported by structured output ontologies, CliBench enables a precise and multi-granular evaluation, offering an in-depth understanding of LLM's capability on diverse clinical tasks of desired granularity. We conduct a zero-shot evaluation of leading LLMs to assess their proficiency in clinical decision-making. Our preliminary results shed light on the potential and limitations of current LLMs in clinical settings, providing valuable insights for future advancements in LLM-powered healthcare.

Related papers

• ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World [21.36586226277893]
  We introduce ClinicalLab, a comprehensive clinical diagnosis agent alignment suite. ClinicalLab includes ClinicalBench, an end-to-end multi-departmental clinical diagnostic evaluation benchmark. We evaluate 17 LLMs and find that their performance varies significantly across different departments.
  arXiv  Detail & Related papers  (2024-06-19T23:44:25Z)
• CLUE: A Clinical Language Understanding Evaluation for LLMs [2.3814275542331385]
  Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. Assessing the models' suitability for this sensitive application area is of utmost importance. We present the Clinical Language Understanding Evaluation (CLUE), a benchmark tailored to evaluate LLMs on clinical tasks.
  arXiv  Detail & Related papers  (2024-04-05T12:51:37Z)
• Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm [15.627870862369784]
  Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis. We propose an automatic evaluation paradigm tailored to assess the LLMs' capabilities in delivering clinical services.
  arXiv  Detail & Related papers  (2024-03-25T06:17:54Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds [32.99251005719732]
  Clinical reasoning refers to the cognitive process that physicians employ in evaluating and managing patients. In this study, we introduce a novel framework, In-Context Padding (ICP), designed to enhance LLMs with medical knowledge.
  arXiv  Detail & Related papers  (2024-03-11T10:53:20Z)
• Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models [59.60384461302662]
  We introduce Asclepius, a novel benchmark for evaluating Medical Multi-Modal Large Language Models (Med-MLLMs) Asclepius rigorously and comprehensively assesses model capability in terms of distinct medical specialties and different diagnostic capacities. We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 5 human specialists.
  arXiv  Detail & Related papers  (2024-02-17T08:04:23Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models [56.36916128631784]
  We introduce MedBench, a comprehensive benchmark for the Chinese medical domain. This benchmark is composed of four key components: the Chinese Medical Licensing Examination, the Resident Standardization Training Examination, and real-world clinic cases. We perform extensive experiments and conduct an in-depth analysis from diverse perspectives, which culminate in the following findings.
  arXiv  Detail & Related papers  (2023-12-20T07:01:49Z)
• Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales [15.362903610463285]
  We present a "reasoning-aware" diagnosis framework that rationalizes the diagnostic process via prompt-based learning. We propose a novel set of criteria for evaluating machine-generated rationales' potential for real-world clinical settings.
  arXiv  Detail & Related papers  (2023-12-12T16:14:45Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models [85.4333256782337]
  VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow. We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
  arXiv  Detail & Related papers  (2021-08-04T17:34:13Z)

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.