  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.
