ReviewPLOS digital health2023
Bias in artificial intelligence algorithms and recommendations for mitigation.
Review in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 186 papers, 7 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
186 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- Artificial intelligence in orthopaedic education, training and research: a systematic review.BMC medical education · 2025Pooled it
- Dietary Patterns for Weight and Glycemic Management in Persons With Type 1 Diabetes: A Meta-analysis of Clinical Trials.The Journal of clinical endocrinology and metabolism · 2025Pooled it
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 2025Pooled it
- Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.Journal of medical systems · 2024Pooled it
- Health Care Professionals' Experience of Using AI: Systematic Review With Narrative Synthesis.Journal of medical Internet research · 2024Pooled it
- Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.Arquivos de neuro-psiquiatria · 2024Pooled it
- Artificial intelligence and hearing health: a global evidence review of biases and equity implications for Africa.Global health action · 2026Review
- Mapping the evidence on artificial intelligence-based clinical decision support systems for undergraduate nursing students: a scoping review protocol.JBI evidence synthesis · 2026Article
- Ethical and Legal Challenges of Partially and Fully Autonomous AI in Healthcare: Reinterpreting Liability and Preserving Trust.Bioethics · 2026Article
- Attitudes Toward Large Language Models in Health Care and Preferences for Their Adoption and Oversight Among Health Care Professionals: Cross-Sectional Survey.Journal of medical Internet research · 2026Article
- Stakeholder Perspectives on Health System Readiness for Integrating Artificial Intelligence into Public Health in Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Deceptive bias measurement in deep learning: Assessing shortcut reliance in TCGA cancer models.PLOS digital health · 2026Article
- Review
- PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI.PLOS digital health · 2026Article
- Artificial Intelligence in Pharmacovigilance: Guiding Principles from the CIOMS Working Group XIV.Drug safety · 2026Review
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Review
- Building a Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care: Protocol for a FAIR-EC Study.JMIR research protocols · 2026Article
- How Can Clinicians Decide if AI-Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?Journal of evaluation in clinical practice · 2026Article
126 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.
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.