ReviewNature medicine2025
The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.
Review in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 2 registered trials, which are not on this map. Cited by 160 papers, 4 of them syntheses that pooled it.
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.
Diagnostic Accuracy of Two Large Language Models Against a Blinded Specialist Consensus Standard in Turkish Emergency Department Notes: A Retrospective Study of 600 Cases
FECAL-AI: Prospective Observational Validation of AI-Based Stool Image Analysis Against Quantitative Fecal Immunochemical Testing for Colorectal Neoplasia Risk Assessment
Who cites it
160 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence.Journal of medical Internet research · 2026Pooled it
- Diagnostic Accuracy of AI in Prediction and Assessment of Compromised Free Flaps: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- Detection and Management of Geographic Atrophy Secondary to Age-Related Macular Degeneration Using Noninvasive Retinal Images and Artificial Intelligence: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Response to letter regarding "artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study".Annals of medicine · 2026Article
- Accountability for large language models in health care.Bulletin of the World Health Organization · 2026Article
- Assessing and mitigating demographic bias in large language models for diagnostic radiology.Japanese journal of radiology · 2026Article
- Audiologist-Guided Multimodal AI for Pure-Tone Audiometry and Tympanometry Interpretation and Reporting.Journal of medical systems · 2026Article
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026Review
- Tomosynthesis and Synthesized-2D Breast AI Outputs in a Biopsy-Referred Cohort: A Diagnostic Study of Agreement and Incremental Decision-Support Value.Journal of imaging informatics in medicine · 2026Article
- Mapping the global landscape of artificial intelligence in pancreatic cancer research: A bibliometric and visualization analysis.Medicine · 2026Article
- Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort.Emergency radiology · 2026Article
- Artificial Intelligence Reporting Guidelines in Radiology: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Laboratory Medicine Decision Support-Beyond Exam Passing: A Blinded 100-Case Text-Based Benchmark of Diagnostic Accuracy, Management Quality, and Safety for ChatGPT, Gemini, and DeepSeek-LLM Decision Support in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2026Article
- Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis.Medeniyet medical journal · 2026Article
- Artificial Intelligence and Digital Technologies in Orthognathic and Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.Dentistry journal · 2026Review
- Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection Across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis.Diagnostics (Basel, Switzerland) · 2026Review
- Slice-Level Deep Learning Classification of Acute Cholecystitis on Contrast-Enhanced CT: A Single-Center Benchmark of Six CNN Architectures.Diagnostics (Basel, Switzerland) · 2026Article
100 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
Abstract
The Standards for Reporting Diagnostic Accuracy (STARD) 2015 statement facilitates transparent and complete reporting of diagnostic test accuracy studies. However, there are unique considerations associated with artificial intelligence (AI)-centered diagnostic test studies. The STARD-AI statement, which was developed through a multistage, multistakeholder process, provides a minimum set of criteria that allows for comprehensive reporting of AI-centered diagnostic test accuracy studies. The process involved a literature review, a scoping survey of international experts, and a patient and public involvement and engagement initiative, culminating in a modified Delphi consensus process involving over 240 international stakeholders and a consensus meeting. The checklist was subsequently finalized by the Steering Committee and includes 18 new or modified items in addition to the STARD 2015 checklist items. Authors are encouraged to provide descriptions of dataset practices, the AI index test and how it was evaluated, as well as considerations of algorithmic bias and fairness. The STARD-AI statement supports comprehensive and transparent reporting in all AI-centered diagnostic accuracy studies, and it can help key stakeholders to evaluate the biases, applicability and generalizability of study findings.
Indexed as
Identifiers
40954311What 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.