ReviewInsights into imaging2023
MAIC-10 brief quality checklist for publications using artificial intelligence and medical images.
Review in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 8 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
34 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Deep learning in the diagnosis, staging, and prognosis of osteonecrosis of the femoral head: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.The Lancet. Digital health · 2026Pooled it
- Availability and transparency of artificial intelligence models in radiology: a meta-research study.European radiology · 2025Pooled it
- Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.European radiology · 2025Pooled it
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks.European radiology · 2024Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study.BMC medical research methodology · 2023Pooled it
- The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.European radiology · 2026Article
- Implementation of an artificial intelligence-based system for mammography in the compulsory medical insurance program: results of a 3-year study.Quantitative imaging in medicine and surgery · 2026Article
- Airway segmentation on CT - A systematic review of machine learning tools.European journal of radiology open · 2026Article
- Automated identification of MRI series using a hierarchical modular machine-learning pipeline.European radiology experimental · 2026Article
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Radiomic-Based Machine Learning Classifiers for HPV Status Prediction in Oropharyngeal Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- Evaluation of Deep Learning Convolutional Neural Networks for Classification of Carcinoma Ex Pleomorphic Adenoma and Pleomorphic Adenoma in Whole-Slide Images.Head and neck pathology · 2025Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): an umbrella review with a comprehensive two-level analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Deep learning in rib fracture imaging: study quality assessment using the Must AI Criteria-10 (MAIC-10) checklist for artificial intelligence in medical imaging.Insights into imaging · 2025Article
- 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
7 authors.
Funding
Abstract
The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as "Clinical need", "Data annotation", "Robustness", and "Transparency" present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging.
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.