Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnostics

TL;DR

This paper presents a decision support system that combines deep learning and ChatGPT to automate medical image diagnostics, aiming to improve accuracy, reduce costs, and assist healthcare providers.

Contribution

It introduces a novel architecture integrating deep learning with ChatGPT for automatic medical image diagnosis, including data processing, model training, and report generation.

Findings

Promising accuracy in automatic diagnosis

Effective integration of deep learning with ChatGPT

Potential to assist healthcare decision-making

Abstract

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.