X-Net the AI Radiologist Assistant

Abstract

The integration of deep learning methodologies into radiology has the potential to revolutionize the diagnosis of chest diseases. However, there is a significant gap between deep learning researchers and medical professionals, which hinders the translation of these advancements into clinical applications. We have created a prototype system that enables medical practitioners to assess the effectiveness of deep learning algorithms in chest X-ray diagnosis. Our system is intuitive and freely accessible, and it can be accessed via web browsers, including mobile devices. The system employs a client-server architecture to deliver code and network weights through a secure URL, while preserving the privacy and confidentiality of patient data. We believe that this prototype system will serve as a catalyst for collaboration and knowledge exchange between deep learning researchers and medical professionals. By providing medical practitioners with a practical tool to evaluate the capabilities of deep learning algorithms, we anticipate enhanced diagnostic accuracy and improved patient outcomes. Moreover, this system is poised to encourage collaborative efforts, enabling the seamless integration of expertise from both domains, ultimately propelling advancements in the field of medical diagnosis of chest diseases using X-ray imaging with AUC score of 76% accuracy.