An Approach to Intelligent Pneumonia Detection and Integration
This paper addresses the challenge of generalizing AI-based pneumonia detection for healthcare providers, which is an incremental step towards broader clinical adoption.
This paper proposes a roadmap for creating and integrating an AI-based pneumonia detection system that aims to overcome challenges in generalizing locally achieved results. It also addresses technical, legal, ethical, and logistical issues with potential solutions.
Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.