ArticleFrontiers in artificial intelligence2026
Evidence-based AI: from trailblazer to trustblazer?
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. Cited by 1 paper.
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
1 citing paper in PubMed.
- The data substrate of exposome intelligence: an interoperability profile for untargeted metabolomics.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agentic AI systems can plan, call tools, and coordinate specialized sub-agents, enabling multi-step scientific workflows that exceed what single-model text generation can reliably deliver. Yet in high-stakes domains such as regulatory science and toxicology, fluent outputs are not sufficient: adoption hinges on traceability, reproducibility, context-of-use validity, and explicit uncertainty communication. This perspective argues that evidence-based medicine and evidence-based toxicology provide a mature epistemic scaffold for making agentic AI trustworthy by design. We propose an Evidence-based Agent Stack that decomposes end-to-end tasks into protocolized roles (question framing, retrieval, screening, extraction, risk-of-bias appraisal, synthesis, mechanistic/causal integration, uncertainty assessment, and evidence-to-decision translation) with mandatory provenance and versioning. Anchoring agentic workflows in systematic review practice, risk-of-bias frameworks, and emerging regulatory principles (e.g., TREAT and e-validation) can turn "
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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.