ArticleiScience2024
A nested model for AI design and validation.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- The limits of debiased clinical language models for cross-hospital generalization.Scientific reports · 2026Article
- UbiQTree: Uncertainty quantification in XAI with tree ensembles.Patterns (New York, N.Y.) · 2026Article
- Ethics training competencies and leadership enable responsible AI in hospitality and tourism.iScience · 2026Article
- Advocating the potential of artificial intelligence for syndrome discovery in syndromic surveillance systems: A scoping review.iScience · 2026Review
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Effects of knowledge and importance on responsibility in human-AI decision making.Scientific reports · 2025Article
- Current applications and future directions in natural language processing for news media and mental health.Scientific reports · 2025Article
- The Way Forward to Embrace Artificial Intelligence in Public Health.American journal of public health · 2025Article
- AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world.Journal of global health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science and AI, preventing a consistent framework. A five-layer nested model for AI design and validation aims to address these issues and streamline AI application design and validation, improving fairness, trust, and AI adoption. This model aligns with regulations, addresses AI practitioners' daily challenges, and offers prescriptive guidance for determining appropriate evaluation approaches by identifying unique validity threats. We have three recommendations motivated by this model: (1) Authors should distinguish between layers when claiming contributions to clarify the specific areas in which the contribution is made and to avoid confusion; (2) authors should explicitly state upstream assumptions to ensure that the context and limitations of their AI system are clearly understood, (3) AI venues should promote thorough testing and validation of AI systems and their compliance with regulatory requirements.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.