AI Startups: The Need for Collaborative Research and Development

Abstract

Background: In recent years, the rapid development of Artificial Intelligence (AI) has had a remarkable impact on the medical imaging domain. However, there remain challenges in utilizing state-of-the-art models in clinical practice. This talk focuses on challenges faced by AI startups in using machine learning in clinical practice. Objectives: 1. How the machine learning methods solve a real clinical problem and what are its opportunities and challenges? 2. What are the future directions of AI in medical imaging? Outline: The first part of the talk provides an overall review of some machine learning models developed for solving medical imaging problems. The second part of the talk presents some of the main challenges in utilizing state-of-the-art machine learning in medical imaging applications. These challenges include interpreting complex models, incorporating causality in our models, working with longitudinal data, model generalization, and robustness. The last part of the talk focuses on opportunities and future directions of AI in medical domain advances such as automatically identifying potential responders to treatment, leading to the possibility of personalized medicine.