Medical imaging is an essential tool for diagnosing various healthcare diseases and conditions. However, analyzing medical images is a complex and time-consuming task that requires expertise and experience. This article aims to design a decision support system to assist healthcare providers and patients in making decisions about diagnosing, treating, and managing health conditions. The proposed architecture contains three stages: 1) data collection and labeling, 2) model training, and 3) diagnosis report generation. The key idea is to train a deep learning model on a medical image dataset to extract four types of information: the type of image scan, the body part, the test image, and the results. This information is then fed into ChatGPT to generate automatic diagnostics. The proposed system has the potential to enhance decision-making, reduce costs, and improve the capabilities of healthcare providers. The efficacy of the proposed system is analyzed by conducting extensive experiments on a large medical image dataset. The experimental outcomes exhibited promising performance for automatic diagnosis through medical images.

翻译：医学影像是诊断各类医疗疾病与健康状况的重要工具。然而，医学图像分析是一项复杂且耗时的任务，需要专业知识和丰富经验。本文旨在设计一个决策支持系统，以协助医疗提供者和患者就疾病诊断、治疗及健康管理做出决策。所提出的架构包含三个阶段：1）数据收集与标注，2）模型训练，3）诊断报告生成。核心思想是在医学图像数据集上训练深度学习模型，以提取四类信息：扫描类型、身体部位、测试图像及结果。随后将这些信息输入ChatGPT以生成自动诊断结果。该系统具有增强决策能力、降低成本并提升医疗提供者效能的潜力。通过在大型医学图像数据集上进行广泛实验，分析了所提系统的有效性。实验结果表明，该系统在通过医学图像实现自动诊断方面展现出良好性能。