Artificial Intelligence in Healthcare: Lost In Translation?
This addresses the gap between AI research and practical healthcare applications, which is crucial for improving patient outcomes and reducing costs, though it is incremental as it builds on existing critiques.
The paper identifies challenges in translating AI research into clinically validated healthcare products, highlighting issues in precision medicine, reproducibility, data, causality, and product development, and proposes solutions to improve this translation.
Artificial intelligence (AI) in healthcare is a potentially revolutionary tool to achieve improved healthcare outcomes while reducing overall health costs. While many exploratory results hit the headlines in recent years there are only few certified and even fewer clinically validated products available in the clinical setting. This is a clear indication of failing translation due to shortcomings of the current approach to AI in healthcare. In this work, we highlight the major areas, where we observe current challenges for translation in AI in healthcare, namely precision medicine, reproducible science, data issues and algorithms, causality, and product development. For each field, we outline possible solutions for these challenges. Our work will lead to improved translation of AI in healthcare products into the clinical setting