A new direction to promote the implementation of artificial intelligence in natural clinical settings

TL;DR

This paper proposes using a clinical benchmark suite to better capture real-world clinical task features, aiming to bridge the gap between AI achievements and practical implementation in natural clinical settings.

Contribution

Introducing a clinical benchmark suite as a new approach to guide AI development for effective real-world clinical application.

Findings

Benchmark suite effectively captures essential clinical task features

Improves alignment of AI systems with real-world clinical needs

Facilitates translation of AI research into clinical practice

Abstract

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.