Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales

• URL: http://arxiv.org/abs/2312.07399v3
• Date: Fri, 10 May 2024 07:24:27 GMT
• Title: Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
• Authors: Taeyoon Kwon, Kai Tzu-iunn Ong, Dongjin Kang, Seungjun Moon, Jeong Ryong Lee, Dosik Hwang, Yongsik Sim, Beomseok Sohn, Dongha Lee, Jinyoung Yeo,
• Abstract summary: 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.
• Score: 15.362903610463285
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical classification or reading comprehension, and under-explore clinical reasoning for disease diagnosis due to the expensive rationale annotation with clinicians. In this work, we present a "reasoning-aware" diagnosis framework that rationalizes the diagnostic process via prompt-based learning in a time- and labor-efficient manner, and learns to reason over the prompt-generated rationales. Specifically, we address the clinical reasoning for disease diagnosis, where the LLM generates diagnostic rationales providing its insight on presented patient data and the reasoning path towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT). We empirically demonstrate LLMs/LMs' ability of clinical reasoning via extensive experiments and analyses on both rationale generation and disease diagnosis in various settings. We further propose a novel set of criteria for evaluating machine-generated rationales' potential for real-world clinical settings, facilitating and benefiting future research in this area.

Related papers

• CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions [16.310913127940857]
  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.
  arXiv  Detail & Related papers  (2024-06-14T11:10:17Z)
• 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)
• Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis [30.943705201552643]
  We propose a framework to model the diagnosis process in the real world by adaptively fusing probability distributions of agents over potential diseases. Our approach requires significantly less parameter updating and training time, enhancing efficiency and practical utility.
  arXiv  Detail & Related papers  (2024-01-29T12:25:30Z)
• Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis [14.17497921394565]
  We propose a medical dialogue generation framework with the Intuitive-then-Analytic Differential Diagnosis (IADDx) Our method starts with a differential diagnosis via retrieval-based intuitive association and subsequently refines it through a graph-enhanced analytic procedure. Experimental results on two datasets validate the efficacy of our method.
  arXiv  Detail & Related papers  (2024-01-12T12:35:19Z)
• A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis [84.6348989654916]
  We propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process. It integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path.
  arXiv  Detail & Related papers  (2023-11-26T14:42:31Z)
• A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics [63.106382317917344]
  We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
  arXiv  Detail & Related papers  (2023-06-01T16:23:47Z)
• Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure [26.565616653685115]
  We present a proof-of-concept system to demonstrate the technical and design feasibility of this approach across three domains. Leveraging Clinical BioBERT, the system can effectively identify clinical trial reports based on lengthy clinical questions. We discuss the idea of designing DST explanations not as specific to a DST or an algorithm, but as a domain-agnostic decision support infrastructure.
  arXiv  Detail & Related papers  (2021-10-31T23:21:25Z)
• BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images [69.41441138140895]
  This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice.
  arXiv  Detail & Related papers  (2021-10-05T19:14:46Z)
• 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)
• Anytime Diagnosis for Reconfiguration [52.77024349608834]
  We introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain.
  arXiv  Detail & Related papers  (2021-02-19T11:45:52Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

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.