ReviewPloS one2019
The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data.
Review in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Acute hypoxemic respiratory failure: from static severity to trajectory-based phenotyping.Intensive care medicine · 2026Article
- Protocol for development of a reporting guideline (TRIPOD-Code) for code repositories associated with diagnostic and prognostic prediction model studies.Diagnostic and prognostic research · 2026Review
- External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges.BMC cancer · 2025Article
- Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review.JAMIA open · 2025Review
- Peroxynitrite-Triggered Carbon Monoxide Donor Improves Ischemic Stroke Outcome by Inhibiting Neuronal Apoptosis and Ferroptosis.Molecular neurobiology · 2024Article
- Diversity and inclusion: A hidden additional benefit of Open Data.PLOS digital health · 2024Article
- A multimodal framework for extraction and fusion of satellite images and public health data.Scientific data · 2024Article
- Artificial Intelligence in Orthodontics: Critical Review.Journal of dental research · 2024Review
- A critical moment in machine learning in medicine: on reproducible and interpretable learning.Acta neurochirurgica · 2024Review
- Article
- A scoping review of the landscape of health-related open datasets in Latin America.PLOS digital health · 2023Article
- Reproducibility of prediction models in health services research.BMC research notes · 2022Article
- Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.Journal of clinical medicine · 2022Article
- A distributed approach to the regulation of clinical AI.PLOS digital health · 2022Article
- The new normal? Redaction bias in biomedical science.Royal Society open science · 2021Article
- Effect of data leakage in brain MRI classification using 2D convolutional neural networks.Scientific reports · 2021Article
- The AIMe registry for artificial intelligence in biomedical research.Nature methods · 2021Article
- Quality and Safety in eHealth: The Need to Build the Evidence Base.Journal of medical Internet research · 2019Article
- Looking beyond the hype: Applied AI and machine learning in translational medicine.EBioMedicine · 2019Review
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
Abstract
Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting analyses. Many of us believe that these developments will lead to significant improvements in patient care. Like many academic disciplines, however, progress is hampered by lack of code and data sharing. In bringing together this PLOS ONE collection on machine learning in health and biomedicine, we sought to focus on the importance of reproducibility, making it a requirement, as far as possible, for authors to share data and code alongside their papers.
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.