ReviewPatterns (New York, N.Y.)2021
Addressing bias in big data and AI for health care: A call for open science.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 319 papers, 12 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
319 citing papers in PubMed, 12 syntheses or guidelines pooled it.
- Integrating Psychosocial Factors into Artificial Intelligence Models for Predicting Addiction Treatment Outcomes: A Systematic Review.European addiction research · 2026Pooled it
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.European journal of neurology · 2026Pooled it
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 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
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey.Frontiers in digital health · 2025Pooled it
- Remaining Human in the Age of Artificial Intelligence: Spiritual and Psychiatric Reflections on Magnifica Humanitas.Journal of religion and health · 2026Article
- An Expert-Derived Data-Driven Approach to Identifying Medicaid Children at Risk of Out-of-Home Placement: Development, Application, and Data-Sharing Considerations.Medical care · 2026Article
- From Francis to Leo XIV: A Scoping Review of Papal Teachings on AI from Pope Francis to Pope Leo XIV and Their Implications for Health Ethics and Governance.Journal of religion and health · 2026Article
- Human Touch vs. Tech Talk: Mental Health Clinicians' Perspectives on Artificial Intelligence in Systemic Practice.Journal of marital and family therapy · 2026Article
- When the algorithm passes the test but fails the patient.PLOS digital health · 2026Article
- A Clinician-Centered Evaluation Framework for Large Language Models in Patient Education: Integrating the Technology Acceptance Model and Medical Condition Regard Scale.JMIR formative research · 2026Article
- When Innovation Leaves People Behind: Reframing Accountability in Commercial Digital Health.Journal of medical Internet research · 2026Article
- Artificial Intelligence for the Prediction of Preeclampsia: Current Evidence, Comparison with Conventional Screening Models, and Future Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
259 more citing papers are in PubMed but not listed here.
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
Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.
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.