Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports

• URL: http://arxiv.org/abs/2407.05758v1
• Date: Mon, 8 Jul 2024 09:08:42 GMT
• Title: Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports
• Authors: Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zhengliang Liu, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang,
• Abstract summary: 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.
• Score: 51.45762396192655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical images and radiology reports are crucial for diagnosing medical conditions, highlighting the importance of quantitative analysis for clinical decision-making. However, the diversity and cross-source heterogeneity of these data challenge the generalizability of current data-mining methods. 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 (AGI) for computer vision, showcasing their potential in the biomedical domain. In this study, we evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets, including 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy), and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements and comprehensive validations remain imperative before clinical deployment.

Related papers

• Exploring the Feasibility of Multimodal Chatbot AI as Copilot in Pathology Diagnostics: Generalist Model's Pitfall [17.9731336178034]
  ChatGPT and other multimodal models have shown promise in transforming medical image analysis through capabilities such as medical vision-language question answering. This study benchmarks the performance of GPT on pathology images, assessing their diagnostic accuracy and efficiency in real-word clinical records. We observe significant deficits of GPT in bone diseases and a fair-level performance in diseases from other three systems.
  arXiv  Detail & Related papers  (2024-09-04T01:30:05Z)
• MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis [28.421857904824627]
  MiniGPT-Med is a vision-language model derived from large-scale language models and tailored for medical applications. It is capable of performing tasks such as medical report generation, visual question answering (VQA), and disease identification within medical imagery. It achieves state-of-the-art performance on medical report generation, higher than the previous best model by 19% accuracy.
  arXiv  Detail & Related papers  (2024-07-04T18:21:10Z)
• Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation [0.0]
  Foundation models (FM) are machine or deep learning models trained on diverse data and applicable to broad use cases. FM offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FM into clinical practice.
  arXiv  Detail & Related papers  (2024-06-26T10:51:44Z)
• Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis [59.35504779947686]
  GPT-4V is OpenAI's newest model for multimodal medical diagnosis. Our evaluation encompasses 17 human body systems. GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy. It faces significant challenges in disease diagnosis and generating comprehensive reports.
  arXiv  Detail & Related papers  (2023-10-15T18:32:27Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• CephGPT-4: An Interactive Multimodal Cephalometric Measurement and Diagnostic System with Visual Large Language Model [4.64641334287597]
  The CephGPT-4 model exhibits excellent performance and has the potential to revolutionize orthodontic measurement and diagnostic applications. These innovations hold revolutionary application potential in the field of orthodontics.
  arXiv  Detail & Related papers  (2023-07-01T15:41:12Z)
• AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation [50.21065317817769]
  We propose an AlignTransformer framework, which includes the Align Hierarchical Attention (AHA) and the Multi-Grained Transformer (MGT) modules. Experiments on the public IU-Xray and MIMIC-CXR datasets show that the AlignTransformer can achieve results competitive with state-of-the-art methods on the two datasets.
  arXiv  Detail & Related papers  (2022-03-18T13:43:53Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.