Ophtha-LLaMA2: A Large Language Model for Ophthalmology

• URL: http://arxiv.org/abs/2312.04906v1
• Date: Fri, 8 Dec 2023 08:43:46 GMT
• Title: Ophtha-LLaMA2: A Large Language Model for Ophthalmology
• Authors: Huan Zhao, Qian Ling, Yi Pan, Tianyang Zhong, Jin-Yu Hu, Junjie Yao, Fengqian Xiao, Zhenxiang Xiao, Yutong Zhang, San-Hua Xu, Shi-Nan Wu, Min Kang, Zihao Wu, Zhengliang Liu, Xi Jiang, Tianming Liu, Yi Shao
• Abstract summary: Large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP) In this study, we build an LLM termed the "Ophtha-LLaMA2" specifically tailored for ophthalmic disease diagnosis. Inference test results show that even with a smaller fine-tuning dataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosis.
• Score: 31.39653268440651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, pre-trained large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP). Prior studies have primarily focused on general and generic domains, with relatively less research on specialized LLMs in the medical field. The specialization and high accuracy requirements for diagnosis in the medical field, as well as the challenges in collecting large-scale data, have constrained the application and development of LLMs in medical scenarios. In the field of ophthalmology, clinical diagnosis mainly relies on doctors' interpretation of reports and making diagnostic decisions. In order to take advantage of LLMs to provide decision support for doctors, we collected three modalities of ophthalmic report data and fine-tuned the LLaMA2 model, successfully constructing an LLM termed the "Ophtha-LLaMA2" specifically tailored for ophthalmic disease diagnosis. Inference test results show that even with a smaller fine-tuning dataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosis compared to other LLMs. It demonstrates that the Ophtha-LLaMA2 exhibits satisfying accuracy and efficiency in ophthalmic disease diagnosis, making it a valuable tool for ophthalmologists to provide improved diagnostic support for patients. This research provides a useful reference for the application of LLMs in the field of ophthalmology, while showcasing the immense potential and prospects in this domain.

Related papers

• CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios [50.032101237019205]
  CliMedBench is a comprehensive benchmark with 14 expert-guided core clinical scenarios. The reliability of this benchmark has been confirmed in several ways.
  arXiv  Detail & Related papers  (2024-10-04T15:15:36Z)
• LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models [38.78576472811659]
  Large vision-language models (LVLMs) have the potential to assist in understanding anatomical information, diagnosing eye diseases, and drafting interpretations and follow-up plans. We benchmarked 13 state-of-the-art LVLM representatives from closed-source, open-source, and medical domains. The results demonstrate a significant performance drop for LVLMs in ophthalmology compared to other domains.
  arXiv  Detail & Related papers  (2024-10-02T14:57:58Z)
• Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis [17.970320199904084]
  We introduce an innovative multi-modal diagnostic pipeline (MDPipe) by employing large language models (LLMs) for ocular surface disease diagnosis. To tackle these challenges, we introduce an innovative multi-modal diagnostic pipeline (MDPipe) by employing large language models (LLMs) for ocular surface disease diagnosis.
  arXiv  Detail & Related papers  (2024-10-01T00:23:05Z)
• 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)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• MedExQA: Medical Question Answering Benchmark with Multiple Explanations [2.2246416434538308]
  This paper introduces MedExQA, a novel benchmark in medical question-answering to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties, we address a major gap in current medical QA benchmarks. Our work highlights the importance of explainability in medical LLMs, proposes an effective methodology for evaluating models beyond classification accuracy, and sheds light on one specific domain, speech language pathology.
  arXiv  Detail & Related papers  (2024-06-10T14:47:04Z)
• 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)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis [2.4554686192257424]
  Large language models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence technology. We evaluate the correctness and accuracy of LLM-generated medical diagnosis with publicly available multimodal multiple-choice questions. We explored a wide range of diseases, conditions, chemical compounds, and related entity types that are included in the vast knowledge domain of Pathology.
  arXiv  Detail & Related papers  (2024-01-28T09:25:12Z)
• A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
  Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language. This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-09T02:55:58Z)
• OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue [7.140551103766788]
  We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM) Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology.
  arXiv  Detail & Related papers  (2023-06-21T11:09:48Z)

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.