ReviewInvestigative ophthalmology & visual science2024
Clinical Evaluation of Artificial Intelligence-Enabled Interventions.
Review in Investigative ophthalmology & visual science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Qualitative and Quantitative Assessment of Four Artificial Intelligence Systems for Nasal Deformity Analysis in Rhinoplasty.Aesthetic plastic surgery · 2026Article
- Lymphedema imaging and AI: A review of diagnostic modalities, biomarkers, and clinical integration.Biomedical journal · 2026Review
- Beyond data and technology: the need for new thinking to enable the era of precision prevention.BMC medicine · 2026Review
- A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.NPJ digital medicine · 2026Article
- Understanding pre-training data effects in retinal foundation models using two large fundus cohorts.Nature communications · 2026Article
- Artificial Intelligence in Medication Adherence: A National Assessment of Knowledge, Attitudes, and Perceptions Among Chronic Disease Patients in Jordan.Patient preference and adherence · 2026Article
- Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.Medical oncology (Northwood, London, England) · 2025Review
- The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa.Frontiers in pediatrics · 2025Review
- Editorial: Place-based evidence for clinical artificial intelligence implementation.Frontiers in health services · 2025Article
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
Artificial intelligence (AI) health technologies are increasingly available for use in real-world care. This emerging opportunity is accompanied by a need for decision makers and practitioners across healthcare systems to evaluate the safety and effectiveness of these interventions against the needs of their own setting. To meet this need, high-quality evidence regarding AI-enabled interventions must be made available, and decision makers in varying roles and settings must be empowered to evaluate that evidence within the context in which they work. This article summarizes good practices across four stages of evidence generation for AI health technologies: study design, study conduct, study reporting, and study appraisal.
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.