Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap

TL;DR

This paper argues that AI in medicine should focus on supporting clinical reasoning and decision making through sociotechnical tools, addressing the translational gap caused by technology-centric approaches.

Contribution

It introduces a sociotechnical framework for AI support tools that enhance doctors' cognitive activities, emphasizing real-world clinical impact over benchmark performance.

Findings

Proposes a sociotechnical model for AI in medicine

Highlights limitations of current technology-centric AI approaches

Emphasizes real-world clinical relevance over benchmark metrics

Abstract

Artificial intelligence promises to revolutionise medicine, yet its impact remains limited because of the pervasive translational gap. We posit that the prevailing technology-centric approaches underpin this challenge, rendering such systems fundamentally incompatible with clinical practice, specifically diagnostic reasoning and decision making. Instead, we propose a novel sociotechnical conceptualisation of data-driven support tools designed to complement doctors' cognitive and epistemic activities. Crucially, it prioritises real-world impact over superhuman performance on inconsequential benchmarks.