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A validated framework for responsible AI in healthcare autonomous systems.

Turki Alelyani¹

¹Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 1988, Saudi Arabia. tnalelyani@nu.edu.sa.

Scientific Reports

|December 19, 2025

Summary

A new framework guides the safe integration of Artificial Intelligence (AI) in healthcare. This validated tool addresses reliability and ethical concerns, offering actionable guidance for AI adoption in clinical and regulatory settings.

Keywords:

Artificial Intelligence Autonomous Systems Framework Healthcare Trustworthiness

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Published on: September 26, 2025

Area of Science:

Healthcare technology
Artificial Intelligence (AI) in medicine
Clinical informatics

Background:

AI-powered autonomous systems face adoption barriers in healthcare due to reliability and safety concerns.
Existing conceptual work requires empirical validation for practical application in clinical and regulatory settings.

Purpose of the Study:

To introduce and empirically validate a refined framework for the safe and responsible integration of AI in healthcare.
To provide actionable guidance for clinicians, developers, regulators, and procurement bodies.

Main Methods:

Developed an initial framework through semi-structured interviews with 15 experts from diverse domains (clinical, technical, ethical, regulatory).
Validated the framework with 10 new participants using quantitative ratings and qualitative feedback.
Ensured alignment with international standards like ISO 21448 and NIST AI Risk Management Framework.

Main Results:

The refined framework achieved high scores for relevance, clarity, and usability during validation.
Participants strongly endorsed the framework's practical utility for responsible AI adoption.
The framework comprises ten dimensions across technical, ethical, and operational categories.

Conclusions:

The validated framework offers a structured tool for evaluating and monitoring autonomous AI systems in healthcare.
It addresses critical issues such as data quality, explainability, fairness, and human-AI collaboration.
The framework facilitates the responsible integration of AI, overcoming adoption barriers in clinical and regulatory contexts.