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

2025-06-05

AI Summary

AI deployment in medicine faces a “translation gap,” primarily due to a technology-centric paradigm fundamentally misaligned with clinicians’ diagnostic reasoning and decision-making practices. Method: This study proposes a socio-technical co-support framework anchored in physicians’ cognitive processes and clinical workflows, introducing a novel clinical-cognition–oriented AI support paradigm that prioritizes real-world utility over context-agnostic benchmark performance. Integrating medical anthropology, cognitive science, and explainable AI (XAI), we design a data-driven tool architecture aligned with clinical reasoning habits and operational constraints. Contribution/Results: We establish a new evaluation framework for AI in healthcare—centered on clinical adaptability, explainability, and human-AI collaboration—thereby providing a systematic theoretical and practical guide for developing trustworthy, clinically integrated AI systems.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and 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.

Problem

• AI lacks real-world clinical impact due to translational gap
• Current AI systems are incompatible with clinical reasoning
• Proposing sociotechnical tools to complement doctors' cognitive tasks

Innovation

• Sociotechnical conceptualization of data-driven tools
• Complements doctors' cognitive and epistemic activities
• Prioritizes real-world impact over benchmark performance