Evidence map›Paper›PMID 41419784›Full record

ArticleScientific reports2025

A validated framework for responsible AI in healthcare autonomous systems.

Turki Alelyani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

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

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI)-powered autonomous systems are increasingly entering healthcare, yet concerns about their reliability, safety, and responsible use present significant barriers to adoption. Building on prior conceptual work, this study introduces a refined and empirically validated framework designed to support the safe and responsible integration of AI in clinical and regulatory contexts. The original framework was developed from semi-structured interviews with 15 experts across clinical, technical, ethical, and regulatory domains, and was subsequently validated through a structured process involving 10 newly recruited participants. Validation combined quantitative ratings and qualitative feedback, yielding consistently high scores for relevance, clarity, and usability, alongside strong endorsement of practical utility. The resulting framework consists of ten dimensions spanning technical, ethical, and operational categories, and is aligned with international standards such as ISO 21448 and the NIST AI Risk Management Framework. By addressing critical issues including data quality, explainability, fairness, and human-AI collaboration, the framework moves beyond abstract principles to provide actionable guidance. It offers clinicians, developers, regulators, and procurement bodies a structured tool to evaluate, monitor, and guide the responsible adoption of autonomous AI systems in healthcare ecosystems.

Indexed as

Artificial IntelligenceDelivery of Health CareHumansArtificial IntelligenceAutonomous SystemsFrameworkHealthcareTrustworthiness

Identifiers

PMID41419784
PMCPMC12738763

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.