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

Problem
This preprint addresses the inadequacy of existing frameworks for establishing trust in clinical AI systems, which often rely solely on model accuracy or user satisfaction. The authors argue that trust must be engineered as a measurable property, incorporating evidence, supervision, and defined operational boundaries for AI autonomy. Current literature lacks a comprehensive approach that integrates these elements, leading to a gap in the deployment of reliable clinical AI systems.

Method
The authors propose a novel framework for trustworthy clinical AI based on three core principles: evidence, supervision, and staged autonomy. The architecture consists of a deterministic core that integrates a patient-specific AI assistant for contextual validation. This is complemented by a multi-tier model escalation mechanism that allows for human oversight in verification and risk management. The framework emphasizes selective verification of clinically critical findings and employs classifier-driven modular prompting to enhance clinical depth while maintaining performance. Trust metrics are introduced, grounded in metrological principles such as measurement uncertainty, calibration, and traceability, enabling a quantitative assessment of trust across the system’s architectural layers.

Results
While specific numerical results are not provided in the abstract, the authors suggest that their framework allows for improved trustworthiness in clinical AI applications compared to traditional black-box models. The proposed trust metrics facilitate a more rigorous evaluation of AI systems, although comparative performance metrics against established baselines are not explicitly detailed in the paper.

Limitations
The authors acknowledge that their framework requires careful implementation and may not be universally applicable across all clinical contexts. They do not address potential challenges in integrating this framework with existing clinical workflows or the computational overhead associated with the multi-tier escalation mechanism. Additionally, the reliance on human oversight may introduce variability in trust assessments, which is not explored in depth.

Why it matters
This work has significant implications for the development of clinical AI systems, as it provides a structured approach to embedding trustworthiness into AI architectures. By emphasizing evidence-based validation and human oversight, the framework could enhance the adoption of AI in clinical settings, ultimately leading to better patient outcomes. The introduction of quantifiable trust metrics may also pave the way for regulatory standards in clinical AI, fostering greater accountability and transparency in AI-assisted medical decision-making.

Authors: Serhii Zabolotnii, Viktoriia Holinko, Olha Antonenko
Source: arXiv:2604.26671
URL: https://arxiv.org/abs/2604.26671v1