ArticleiMetaOmics2024
STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability.
Article in iMetaOmics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed.
- Using randomization to compare AI and expert-generated formative assessment questions in medical education.Medical education online · 2026Trial
- ChatGPT's role in alleviating anxiety in total knee arthroplasty consent process: a randomized controlled trial pilot study.International journal of surgery (London, England) · 2025Trial
- Impact of guideline-based prompting on the large language model performance in dental trauma management clinical decision-making.Odontology · 2026Article
- Performance of multimodal large language models on image-based surgical anatomy, anatomical pathology, and radiology questions.Anatomical sciences education · 2026Article
- RoBuster-Corpus Annotated With Risk of Bias Text Spans in Randomized Controlled Trials in Physiotherapy and Rehabilitation: Corpus Development and Annotation Study.JMIR formative research · 2026Article
- Deciphering AI-chatbots utilization in clinical practice, research and education among Chinese medical students in generative AI era: a cross-sectional study.BMC medical education · 2026Article
- Integrating digital solutions improves mental health management in cancer care.Communications medicine · 2026Review
- Mapping and Quality Appraisal of Artificial Intelligence Preferential Reporting Checklists, Items, Guidelines, and Consensus in Healthcare: An Altmetric, Bibliometric, and Systematic Review.International journal of dentistry · 2026Article
- Article
- RELICT-NI: Replica Detection in Synthetic Neuroimaging-A Study on Noncontrast CT and Time-of-Flight MRA.Neuroinformatics · 2025Article
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Virtual case reasoning and AI-assisted diagnostic instruction: an empirical study based on body interact and large language models.BMC medical education · 2025Article
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
- Evaluating the influence of prompt formulation on the reliability and repeatability of ChatGPT in implant-supported prostheses.PloS one · 2025Article
- iMetaOmics: Advancing human and environmental health through integrated meta-omics.iMetaOmics · 2024Article
- The Evaluation of Generative AI Should Include Repetition to Assess Stability.JMIR mHealth and uHealth · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
35 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32-item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org.
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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.