ArticleScientific reports2022
On evaluation metrics for medical applications of artificial intelligence.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 365 papers, 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
365 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- Integrating Machine Learning into Myositis Research: a Systematic Review.Clinical reviews in allergy & immunology · 2025Pooled it
- Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Pooled it
- A hybrid CNN-GNN and multitask learning pipeline for improving mild diabetic retinopathy sensitivity.MethodsX · 2026Article
- Artificial intelligence in cardiovascular medicine: a practical guide for clinicians and researchers.European heart journal. Digital health · 2026Review
- Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.Advanced healthcare materials · 2026Review
- Integrating Clinical Priorities into the Technical Validation of Machine Learning Classifiers: A Generalizable Framework Demonstrated on Opioid Misuse Screening.Journal of clinical medicine · 2026Article
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- Automated tooth numbering on panoramic radiographs versus cone-beam computed tomographs: a diagnostic accuracy study of a commercial artificial intelligence system.Dento maxillo facial radiology · 2026Article
- Spatial analysis reveals cellular microenvironments and mechanisms of inflammation and injury in acute interstitial nephritis.Nature communications · 2026Article
- A mortality model for relapsed or refractory multiple myeloma: emulation of subjective life expectancy criteria from a clinical trial in real-world data.Annals of hematology · 2026Article
- Comparison of Gram stain interpretation accuracy between a computer-aided diagnosis app and microbiology specialists in blood culture samples.Microbiology spectrum · 2026Observational
- Knowledge and Awareness of Performance Metrics in Dental Image Analysis With Artificial Intelligence: A Cross-Sectional Survey of Researchers and Clinicians.International dental journal · 2026Article
- A machine learning approach to conglomerate multi-domain features of cardiac aging.European heart journal. Digital health · 2026Article
- Image Resolution's Impact on Artificial Intelligence & Human Accuracy in Full-Mouth Radiographic Analysis.International dental journal · 2026Article
- Deep Learning Pose Estimation for Phenotyping of Co-Occurring Hyperkinetic Movement Disorders.Annals of clinical and translational neurology · 2026Article
- Evaluating performance and potential clinical benefit of the Swedish On Scene Injury Severity Prediction (OSISP) model for prehospital field triage on Norwegian trauma data.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
305 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.
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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.