ADVANCES IN AI FOR PULMONARY DISEASE DIAGNOSIS USING LUNG X-RAY SCAN AND CHEST MULTI-SLICE CT SCAN

Worldwide, lung cancer, pneumonia, chronic obstructive pulmonary disease, and interstitial lung disease continue to endanger people's health. Conventional methods of diagnosing issues in the lungs often rely on chest x-rays as well as CT and ultra sound scans which need human interpretation . One area that is often taken too slowly and can at times lead to mistakes. This article looks into the most recent developments in deep learning automation systems designed for the diagnosis of chest abnormalities due to pulmonary diseases. Determining the effect of deep learning, namely convolutional neural networks, on the precision and effectiveness of clinical disease detection is the study's goal. Multiple stride deep learning models aimed at automatic recognition of pulmonary diseases will be captured with an emphasis on competition between various architectures of controlled neural nets and their algorithms. Also considered are other problems at the interface of deep learning and large medical datasets such as lack of properly labeled data, justification of model based predictions, and design of clinical decision support systems with user-hoping intelligence. Additionally, it analyzes the pioneering AI-enhanced pulmonary diagnostics and what public health outcomes can be achieved with improved timeliness and accuracy in clinical diagnosis. Afterward, we suggest critical issues that need to be addressed, such as the model verification assumptions and ethical implications regarding the use of global medical data and imaging.