ReviewNPJ digital medicine2025
Is regulatory science ready for artificial intelligence?
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- A tri-scale in silico framework integrating pharmacovigilance and mechanistic modeling suggests tepotinib-associated acute kidney injury risk.Renal failure · 2026Article
- Artificial Intelligence-Powered One Health: A Predictive Framework for Managing Persistent Chemical Threats across Human, Animal, and Environmental Systems.Global challenges (Hoboken, NJ) · 2026Review
- Artificial Intelligence for Regulatory Evidence: A Systematic Document Analysis of European Medicines Agency Regulatory Advice and Public Reports.Clinical pharmacology and therapeutics · 2026Article
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.Archives of toxicology · 2026Article
- Data-driven medical devices and the EU MDR: mapping gaps in standards for regulatory compliance.npj health systems · 2026Review
- Beyond the hype: navigating the real-world applications of artificial intelligence (AI) in healthcare.Medical review (2021) · 2026Review
- Charting exposomethics: a roadmap for the ethical foundations of the human exposome project.Human genomics · 2026Review
- Next generation validation for next generation risk assessment.Frontiers in toxicology · 2026Review
- The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics.Frontiers in artificial intelligence · 2026Article
- A pragmatic workflow for integrating large language models into toxicological risk assessment.Frontiers in toxicology · 2026Article
- Evidence-based AI: from trailblazer to trustblazer?Frontiers in artificial intelligence · 2026Article
- Green toxicology only becomes beautiful through AI.Frontiers in chemistry · 2026Review
- Future-Proofing European Pharmaceutical Regulatory and Market Access Practices Based on EU Learnings from the COVID-19 Pandemic: Insights from Multi-Stakeholder Interviews.Therapeutic innovation & regulatory science · 2026Article
- AI snake oil? A risk/benefit analysis for toxicology.Frontiers in artificial intelligence · 2026Article
- Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.Current environmental health reports · 2025Review
- Addressing the current challenges in the clinical application of AI-based Radiomics for cancer imaging.Frontiers in medicine · 2025Review
- AI, agentic models and lab automation for scientific discovery - the beginning of scAInce.Frontiers in artificial intelligence · 2025Review
- NAMazing: Déjà Vu at the lab bench - Why animal-free science is the new automobile.NAM journal · 2025Article
- Mechanistic read-across comes of age: a comparative appraisal of EFSA 2025 guidance, ECHA's RAAF, and good read-across practice.Frontiers in toxicology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trust is key in AI for regulatory science, but its definition is debated. If AI models use different features yet perform similarly, which should be trusted? If scientific theories must be testable, how critical is explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful AI adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness its potential for regulatory responsibilities in the evolving 21st-century landscape.
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.