ArticleAccountability in research2026
Guidelines needed for the use of AI in the preparation or review of IRB, IBC, and IACUC applications.
Article in Accountability in research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 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
3 citing papers in PubMed.
- Generative AI can and should accelerate research evaluation reform to better recognize 'distinctly human contributions'.Research Evaluation · 2026Article
- AI-assisted crop improvement: new design tools within established regulatory frameworks.Frontiers in plant science · 2026Article
- Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model.Frontiers in digital health · 2026Article
Corrections and comments
- Erratum issuedCorrection.2026
Authors and funding
7 authors.
Funding
Abstract
Three oversight bodies review research proposals to help ensure the safe and responsible conduct of biomedical research, each focusing on unique aspects of research ethics: institutional review boards (IRBs), institutional biosafety committees (IBCs), and institutional animal care and use committees (IACUCs). The role of artificial intelligence (AI) in research oversight is rapidly expanding, specifically when preparing and reviewing applications. Although using AI may reduce administrative costs and burdens, it also may create new concerns since AI tools can make mistakes of fact and reasoning, and are susceptible to bias. Furthermore, outsourcing ethical planning and oversight of research to AI could compromise ethical understanding. Although the arguments for/against using AI in preparation or review of IRB, IBC, or IACUC differ fundamentally from those concerning AI use in manuscript writing/peer review, currently there is minimal guidance about the responsible use of AI in research oversight from government agencies, professional organizations, universities, hospitals, and other entities that conduct research. We argue that 1) to minimize the risks of using AI in research oversight, additional guidance is urgently needed; and 2) humans must always be the final decider because ethical planning and oversight involve value judgments that should not be outsourced to AI.
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.