Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia
- URL: http://arxiv.org/abs/2409.15377v1
- Date: Fri, 20 Sep 2024 06:47:36 GMT
- Title: Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia
- Authors: Elisa Castagnari, Lillian Muyama, Adrien Coulet,
- Abstract summary: In practice, clinicians achieve a diagnosis by following a sequence of steps, such as laboratory exams, observations, or imaging.
The pathways to reach diagnosis decisions are documented by guidelines authored by expert organizations, which guide clinicians to reach a correct diagnosis through these sequences of steps.
Our study aimed to develop pathways similar to those that can be obtained in clinical guidelines.
We tested three Large Language Models (LLMs) -Generative Pretrained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA), and Mistral -on a synthetic yet realistic dataset to differentially diagnose anemia and its subtypes.
- Score: 0.8602553195689511
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In practice, clinicians achieve a diagnosis by following a sequence of steps, such as laboratory exams, observations, or imaging. The pathways to reach diagnosis decisions are documented by guidelines authored by expert organizations, which guide clinicians to reach a correct diagnosis through these sequences of steps. While these guidelines are beneficial for following medical reasoning and consolidating medical knowledge, they have some drawbacks. They often fail to address patients with uncommon conditions due to their focus on the majority population, and are slow and costly to update, making them unsuitable for rapidly emerging diseases or new practices. Inspired by clinical guidelines, our study aimed to develop pathways similar to those that can be obtained in clinical guidelines. We tested three Large Language Models (LLMs) -Generative Pretrained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA), and Mistral -on a synthetic yet realistic dataset to differentially diagnose anemia and its subtypes. By using advanced prompting techniques to enhance the decision-making process, we generated diagnostic pathways using these models. Experimental results indicate that LLMs hold huge potential in clinical pathway discovery from patient data, with GPT-4 exhibiting the best performance in all conducted experiments.
Related papers
- MAGDA: Multi-agent guideline-driven diagnostic assistance [43.15066219293877]
In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists.
In this work, we introduce a new approach for zero-shot guideline-driven decision support.
We model a system of multiple LLM agents augmented with a contrastive vision-language model that collaborate to reach a patient diagnosis.
arXiv Detail & Related papers (2024-09-10T09:10:30Z) - RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment [54.91736546490813]
We introduce the RuleAlign framework, designed to align Large Language Models with specific diagnostic rules.
We develop a medical dialogue dataset comprising rule-based communications between patients and physicians.
Experimental results demonstrate the effectiveness of the proposed approach.
arXiv Detail & Related papers (2024-08-22T17:44:40Z) - Towards Evaluating and Building Versatile Large Language Models for Medicine [57.49547766838095]
We present MedS-Bench, a benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts.
MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation.
MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks.
arXiv Detail & Related papers (2024-08-22T17:01:34Z) - Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses [0.2995925627097048]
This study evaluates each model diagnostic abilities by interpreting a user symptoms and determining diagnoses that fit well with common illnesses.
GPT-4 demonstrates higher diagnostic accuracy from its deep and complete history of training on medical data.
Gemini performs with high precision as a critical tool in disease triage, demonstrating its potential to be a reliable model.
arXiv Detail & Related papers (2024-05-09T15:12:24Z) - Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus [1.7965876401882177]
We formulate the task of diagnosis as a sequential decision-making problem.
We study the use of Deep Reinforcement Learning (DRL) algorithms to learn the optimal sequence of actions to perform.
We develop two clinical use cases: Anemia diagnosis and Systemic Lupus Erythematosus diagnosis.
arXiv Detail & Related papers (2024-04-09T00:07:16Z) - Conversational Disease Diagnosis via External Planner-Controlled Large Language Models [18.93345199841588]
This study presents a LLM-based diagnostic system that enhances planning capabilities by emulating doctors.
By utilizing real patient electronic medical record data, we constructed simulated dialogues between virtual patients and doctors.
arXiv Detail & Related papers (2024-04-04T06:16:35Z) - 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) - 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) - 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) - 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.