Over the past decade, Machine Learning and AI have transformed numerous facets of Medical Imaging. Although initially met with skepticism, the imaging community is increasingly embracing the significant advantages that Deep Learning offers.
The foundation of any successful AI-driven solution lies in a robustly trained model and a seamless deployment process. Among the key challenges to achieving impactful software are (1) ensuring product-market fit and (2) evolving from a lab prototype to a minimum viable product, ultimately reaching a regulatory-approved, widely adopted tool.
This presentation will explore various use cases and highlight examples of common pitfalls faced by early-stage software.
We’ll also discuss how today’s AI software developers can successfully bridge these gaps to achieve broader adoption and impact.