A new direction to promote the implementation of artificial intelligence in natural clinical settings
This work tackles the problem of ineffective AI implementation in healthcare for clinicians and developers, though it appears incremental as it builds on existing benchmarking concepts.
The paper addresses the gap between AI achievements in clinical research and their implementation in natural clinical settings by proposing a clinical benchmark suite to capture essential real-world task features, aiming to guide AI development for practical use.
Artificial intelligence (AI) researchers claim that they have made great `achievements' in clinical realms. However, clinicians point out the so-called `achievements' have no ability to implement into natural clinical settings. The root cause for this huge gap is that many essential features of natural clinical tasks are overlooked by AI system developers without medical background. In this paper, we propose that the clinical benchmark suite is a novel and promising direction to capture the essential features of the real-world clinical tasks, hence qualifies itself for guiding the development of AI systems, promoting the implementation of AI in real-world clinical practice.