ArticleSystematic reviews2022
Exponential growth of systematic reviews assessing artificial intelligence studies in medicine: challenges and opportunities.
Article in Systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis.International journal of environmental research and public health · 2022Pooled it
- Ethical and responsible use of artificial intelligence in medical education.International journal of emergency medicine · 2026Review
- Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study.Journal of global health · 2026Article
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
- PulmoClass-3DAtt: A Self-Attention Network for Classification of COPD, PRISm and Normal.International journal of chronic obstructive pulmonary disease · 2026Article
- CarD-T: an automated pipeline for the nomination and analysis of potential human carcinogens.Carcinogenesis · 2025Article
- Chatbot -assisted self-assessment (CASA): Co-designing an AI -powered behaviour change intervention for ethnic minorities.PLOS digital health · 2025Article
- CarD-T: Interpreting Carcinomic Lexicon via Transformers.medRxiv : the preprint server for health sciences · 2024Article
- Ariadne's thread in the age of artificial intelligence: parallels between the greek myth and WAME's recommendations.Colombia medica (Cali, Colombia)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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evidence-based medicine (EBM) movement is stepping up its efforts to assess medical artificial intelligence (AI) and data science studies. Since 2017, there has been a marked increase in the number of published systematic reviews that assess medical AI studies. Increasingly, data from observational studies are used in systematic reviews of medical AI studies. Assessment of risk of bias is especially important in medical AI studies to detect possible "AI bias".
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.