mlsanityData to 14 Oct 2026

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

arXiv 2506.05030 ↗First posted 5 Jun 2025cs.HC cs.AI cs.CYcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 18 of its 147 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

18 of 147 references.

References · 18

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  3. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  4. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
  5. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
  6. 2020Secure and Robust Machine Learning for Healthcare: A Surveycited by 80
  7. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 66
  8. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  9. 2022The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligencecited by 42
  10. 2023Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Carecited by 37
  11. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  12. 2017Data Readiness Levelscited by 13
6 more
  1. 2023Finding Counterfactually Optimal Action Sequences in Continuous State Spacescited by 13
  2. 2023Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communicationcited by 9
  3. 2023Why is plausibility surprisingly problematic as an XAI criterion?cited by 9
  4. 2023(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibilitycited by 6
  5. 2023Navigating Explanatory Multiverse Through Counterfactual Path Geometrycited by 5
  6. 2024Clinicians' Voice: Fundamental Considerations for XAI in Healthcarecited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 11.

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  1. 2025Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoningcited by 14
  2. 2026RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomographycited by 6
  3. 2025Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environmentscited by 2
  4. 2026DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphscited by 1
  5. 2026The Doctor Will (Still) See You Now: On the Structural Limits of Agentic AI in Healthcarecited by 1
  6. 2026MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Modelscited by 1
  7. 2025DiscoVerse: Multi-Agent Pharmaceutical Co-Scientist for Traceable Drug Discovery and Reverse Translationcited by 0
  8. 2026SkinFlow: Efficient Information Transmission for Open Dermatological Diagnosis via Dynamic Visual Encoding and Staged RLcited by 0
  9. 2026Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisionscited by 0
  10. 2026Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Makingcited by 0
  11. 2026From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Developmentcited by 0
  12. 2026Enhancing Clinician Decision-Making via Uncertainty-Aware Multi-Expert Fusion for Stroke Rehabilitationcited by 0
2 more
  1. 2026Physiological World Models for Human State Transitionscited by 0
  2. 2026Trust Hinders Bidirectional Human-AI Alignmentcited by 0