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Clinical AI trust framework emphasizes evidence, supervision, and staged autonomy

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

A new framework proposes that trust in clinical AI should be a measurable system property, not just based on accuracy or user impression. The approach combines a deterministic core with an AI assistant for validation, an escalation mechanism, and human oversight. This system aims to operationalize trust through quantifiable metrics derived from metrology, focusing on evidence, supervision, and staged autonomy. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Proposes a new architectural approach to building trust in clinical AI systems, moving beyond simple accuracy metrics.

RANK_REASON Academic paper proposing a new framework for clinical AI trust.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Serhii Zabolotnii, Viktoriia Holinko, Olha Antonenko ·

    From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy

    arXiv:2604.26671v1 Announce Type: new Abstract: Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in eviden…

  2. arXiv cs.CL TIER_1 · Olha Antonenko ·

    From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy

    Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In medicine, trust must be engineered as a measurable system property grounded in evidence, supervision, and operational boundaries of A…

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