An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems

TL;DR

This paper provides a comprehensive overview of the clinical AI model development life cycle, illustrated with a detailed case study on detecting aortic aneurysms in CT scans, aiming to guide healthcare AI deployment.

Contribution

It offers a generalizable development framework for clinical AI models and shares an in-depth case study to aid healthcare institutions in AI deployment.

Findings

A broadly accessible development life cycle framework is proposed.

An in-depth case study on deep learning for aortic aneurysm detection.

Insights to improve AI deployment success in healthcare.

Abstract

Healthcare is one of the most promising areas for machine learning models to make a positive impact. However, successful adoption of AI-based systems in healthcare depends on engaging and educating stakeholders from diverse backgrounds about the development process of AI models. We present a broadly accessible overview of the development life cycle of clinical AI models that is general enough to be adapted to most machine learning projects, and then give an in-depth case study of the development process of a deep learning based system to detect aortic aneurysms in Computed Tomography (CT) exams. We hope other healthcare institutions and clinical practitioners find the insights we share about the development process useful in informing their own model development efforts and to increase the likelihood of successful deployment and integration of AI in healthcare.