Collaborative AI Research in Medical Imaging: Trends and Challenges

Abstract

Background: Artificial intelligence research and development are speeding up after sound evidence proving the examples of reliability of automated tools to increase speed, accuracy, and reproducibility of imaging services. Who should lead the process? Objectives: By listening to this lecture, the audience is expected to: Outline: The AI research and development is framed by a multi-step lifecycle before being accepted for practical clinical use. Finding the most needed solution for a real clinical problem is probably the most important ring in the chain. But, the next steps are also critically important: collecting the appropriate dataset, annotating the data, selecting the best AI architecture to address the question in mind, stepwise training, testing and improvement of the model, integrating the tool with currently used IT tools in clinical environment, user interface justification, and regulatory approval and marketing that are the others rings of the chain. However, one important issue is to find out who should take care of each step. Regardless of roles and names, AI research needs hardware, software, service and leadership infrastructure to pave the road for research. High-performance computing tools, software applications to run the model, data repositories, clinical tagging services, and validation/licensing services are needed to make AI research a continuous, productive, and improving process.