Sep 2026· Annals of Biomedical Engineering· 0 citations· 12 references
Medicine
TL;DR
It is argued that each of the four challenges facing responsible development is addressed by a clinical testing harness: a structured evaluation environment comprising scenario libraries built from clinical edge cases, full-trajectory observability, explicit escalation testing, and staged evidence thresholds tied to scope of practice.
A framework that treats diagnosis as a coordination problem rather than a modeling one is described and improved on both across accuracy, F1-score, explanation faithfulness and clinician-rated trust, at an added latency of roughly five seconds per case.
Ganesh Dagadu Puri· Natural Resources for Human...· 0 citations
Abstract Clinical artificial intelligence (AI) has advanced rapidly, with frontier large language models now matching or exceeding physician performance on simulated diagnostic reasoning and clinical decision-support tasks. Yet adoption has outpaced the evidence base: fewer than 5% of cleared U.S. Food and Drug Adminis...
John Emmett Worth, Anastasia Perez, David Wu et al.· BMJ digital health & AI· 1 citation
Clinical AI systems increasingly match or exceed clinicians on some diagnostic benchmarks. Yet this reveals little about how AI output functions within clinical reasoning or how repeated reliance affects clinicians' independent capability. This paper proposes Bounded Reciprocal Adaptation for Clinician Engagement (BRAC...
C. Greengrass· Frontiers in Digital Health· 0 citations
Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected evidence rather than the ability to seek it across clinical records and longitudinal imaging. We propose CASE: a series of role-specific Clinical Agents for Seeking Evide...
Min-Ye Shao, Chao-Hui Yu, Yi-Xuan Wu et al.· 0 citations
Summary Artificial intelligence (AI) affects clinical trials in two distinct but overlapping ways: as the intervention under evaluation and as infrastructure supporting trial design, recruitment, monitoring, endpoint assessment, analysis, and reporting. In this manuscript, we define AI-as-intervention as AI whose outpu...
A. Armoundas, C. Tarabanis, J. Loscalzo· EClinicalMedicine· 0 citations
This article proposes seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions.
Alaa Abdelqader, M. Alkhateeb, Abdullah Al-Marrawi et al.· Avicenna Journal of Medicine· 0 citations
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