ReviewEBioMedicine2021
Ensuring that biomedical AI benefits diverse populations.
Review in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 4 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
58 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Examining the Role of AI in Changing the Role of Nurses in Patient Care: Systematic Review.JMIR nursing · 2025Pooled it
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.Bioinformatics (Oxford, England) · 2026Article
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- Hubris in the age of intelligent medicine-from clinical hierarchies to algorithmic amplification.NPJ digital medicine · 2026Review
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Modelling Skin Pigmentation Using the Monte Carlo Technique: A Review.Sensors (Basel, Switzerland) · 2026Review
- Skin Tone in Hyperspectral Imaging and Its Implications for Fairness in AI.Journal of biophotonics · 2026Article
- Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring.Mutagenesis · 2026Review
- Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.The American journal of bioethics : AJOB · 2026Article
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- Risk of bias in machine learning and statistical models to predict height or weight: a systematic review in fetal and paediatric medicine.Diagnostic and prognostic research · 2025Review
- AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review.JMIR mHealth and uHealth · 2025Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Article
- Review
- Engaging Underserved Populations in Health Preference Research: Challenges and Strategies.The patient · 2025Article
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges.
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.