Evidence map›Paper›PMID 42564266›Full record

ArticleFrontiers in artificial intelligence2026

The VIBE-HI framework: a conceptual model for evaluating vibe coding appropriateness, quality, and safety in health informatics.

Ahmed Alqheedan, Saleh Alzughaibi

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Ahmed AlqheedanDepartment of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia.
Saleh AlzughaibiDepartment of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Vibe coding-generating software through natural-language prompts to large language models without reviewing the underlying code-has moved rapidly from consumer technology into peer-reviewed clinical applications. By early 2026, clinicians had published vibe-coded teaching tools, a validated clinical nomogram, and an end-to-end omics platform built in under 10 minutes for under two dollars. Collins Dictionary named vibe coding its 2025 Word of the Year. No governance framework currently addresses the practice in healthcare. Objective: To introduce VIBE-HI, a health-informatics-specific framework for evaluating the appropriateness, quality, and safety of vibe coding across clinical contexts, and to specify its decision logic, quality constructs, and regulatory mapping in operational detail. Methods: VIBE-HI was developed as a conceptual framework through a structured, theory-informed narrative synthesis of three literatures-emerging biomedical vibe-coding reports, empirical software-engineering and security research on AI-generated code and established sociotechnical health-informatics theory and software-quality standards-following recognized conceptual-framework methodology. It was refined through illustrative application to four published clinician-built tools. This is a conceptual contribution; it is not a systematic review or a consensus (Delphi) study, and formal empirical validation is identified as the next step. Results: VIBE-HI organizes governance into three sequential layers. (1) Risk and Role Stratification assign one of four risk tiers-Green, Yellow, Orange, Red-and a matched clinician-developer role, from prototype to requirements analyst, using four criteria combined by an explicit dominant-criterion rule. (2) Quality and Validation extend ISO/IEC 25010:2023 with three measurable constructs-Code Provenance Transparency, Comprehension Coverage, and Hallucination Resilience-each with defined indicators and tier-dependent thresholds. (3) Compliance and Governance maps HIPAA, IEC 62304, FDA SaMD criteria, and the EU AI Act onto each tier and binds a named accountability owner. The framework treats comprehension abdication-the structural surrender of understanding to a generative system-as the core sociotechnical hazard distinguishing vibe coding from prior AI-assisted development, grounded in the automation-bias, responsibility-gap, and sociotechnical-systems literatures. Conclusion: Clinical vibe coding needs risk-stratified governance now, before largely invisible adoption outpaces the field's capacity to assess it. VIBE-HI offers an architecture institutions can apply immediately and provides a clear pathway for empirical validation, beginning with a modified-Delphi consensus study and stakeholder review.

Indexed as

AI governanceclinical software safetyclinician-developerscomprehension abdicationhealth informaticslarge language modelsvibe coding

Identifiers

PMID42564266
PMCPMC13442437

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.