ArticleJournal of medical systems2021
Machine Learning for Health: Algorithm Auditing & Quality Control.
Article in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- 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
- Explainable AI needs formalization.NPJ artificial intelligence · 2026Review
- AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.Nature communications · 2025Article
- Statistical process control for performance monitoring and continuous quality assurance of deep learning segmentations in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?Pathogens (Basel, Switzerland) · 2025Review
- Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ (Clinical research ed.) · 2025Article
- Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review.PLOS digital health · 2024Article
- Machine learning in health financing: benefits, risks and regulatory needs.Bulletin of the World Health Organization · 2024Article
- Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare.Frontiers in digital health · 2024Article
- Beyond ideals: why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it.AI & society · 2024Article
- Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework.Journal of the American Medical Informatics Association : JAMIA · 2023Review
- The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Clinical Artificial Intelligence: Design Principles and Fallacies.Clinics in laboratory medicine · 2023Review
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- The RETA Benchmark for Retinal Vascular Tree Analysis.Scientific data · 2022Article
- Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins?Frontiers in digital health · 2022Article
- Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
39 authors at 20 institutions in 10 countries.
Funding
Abstract
Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.
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.