ReviewPLOS digital health2025
AI-driven healthcare: Fairness in AI healthcare: A survey.
Review in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 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
34 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal.Journal of medical Internet 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
- An interdisciplinary framework for artificial intelligence, precision medicine, and ethical governance in periodontal care: a systematic review.BMC oral health · 2026Pooled it
- Advancements in artificial intelligence transforming medical education: a comprehensive overview.Medical education online · 2025Pooled it
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Integrating AI-Driven Diagnostics in Arrhythmia Care to Enhance Patient Outcomes: A Narrative Review.Cureus · 2026Review
- Trustworthy AI models to predict prolonged hospital length-of-stay in orthopedic surgery.npj health systems · 2026Article
- Next-Generation Bionic Sensors for Small Molecule Detection: Integrating Synthetic Biology, Nanomaterials, and Artificial Intelligence.Micromachines · 2026Review
- Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.Scientific reports · 2026Article
- Artificial Intelligence Adoption in Healthcare Practice and Research: A Cross-Sectional Study of Knowledge, Attitudes, and Practices in Balochistan, Pakistan.Health science reports · 2026Article
- Article
- Hubris in the age of intelligent medicine-from clinical hierarchies to algorithmic amplification.NPJ digital medicine · 2026Review
- Review
- Addressing Research Gaps in Early Childhood Caries: A Comprehensive Review.Dentistry journal · 2026Review
- Machine learning-enhanced behavioural approach to detecting reactions to sound in infants and toddlers: proof-of-concept study.International journal of audiology · 2026Article
- Ethical Knowledge, Challenges, and Institutional Strategies Among Medical AI Developers and Researchers: Focus Group Study.Journal of medical Internet research · 2026Article
- HCLmNet: A unified hybrid continual learning strategy multimodal network for lung cancer survival prediction.PloS one · 2026Article
- Auditing fairness in clinical AI systems using provenance-based simulation: a comparative and regulatory perspective.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Global English-language-dominated discourse on artificial intelligence in healthcare: a three-year longitudinal analysis of the #AIinHealthcare movement on X.Frontiers in digital health · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.
Identifiers
What Socratic 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 Socratic graph.