ArticleFrontiers in artificial intelligence2020
Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 3 of them syntheses that pooled it.
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Who cites it
99 citing papers in PubMed, 3 syntheses or guidelines pooled it, 270 citations in OpenAlex.
- Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence.Frontiers in medicine · 2022Pooled it
- Human Dignity and Artificial Intelligence in Healthcare: A Basis for a Catholic Ethics on AI.Journal of religion and health · 2026Article
- Reframing Justice in Healthcare AI: An Ubuntu-Based Approach for Africa.Developing world bioethics · 2026Article
- Harnessing Artificial Intelligence in Health Research in Low-Income and Middle-Income Countries: Potential and Caution.Mayo Clinic proceedings. Digital health · 2026Review
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- When evidence meets artificial intelligence.Lancet regional health. Americas · 2026Review
- Moral diversity and the challenge of responsibility in AI-CDSS.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- Hubris in the age of intelligent medicine-from clinical hierarchies to algorithmic amplification.NPJ digital medicine · 2026Review
- Research priorities for improved pandemic and epidemic intelligence.The Lancet. Global health · 2026Review
- Traditional, complementary, and integrative medicine in a rapidly digitalizing research environment: ethics and intellectual property considerations.BMC complementary medicine and therapies · 2026Article
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- Comparative analysis of large language models as decision support tools in oral pathology.Scientific reports · 2026Article
- Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation.JAMIA open · 2026Article
- Moving toward equitable juvenile justice risk assessments for adolescents: Considering clinical, community, and statistical fairness.Psychological assessment · 2026Article
- Translating algorithmic fairness into health practice: formative evaluation of the fairness-to-action framework.Frontiers in artificial intelligence · 2026Article
- Advancing health equity in proactive health management: from data underrepresentation and algorithmic bias to a closed-loop governance framework.Frontiers in public health · 2026Review
- Navigating ethical, regulatory, and implementation barriers to AI in healthcare: pathways toward inclusive digital health in low-resource settings-a scoping review.Frontiers in digital health · 2026Review
- Machine learning-based multicenter prediction of postoperative sepsis in emergency colon cancer: role of surgical approach and inflammatory markers.Frontiers in oncology · 2026Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Low- and Middle- Income Countries (LMICs), machine learning (ML) and artificial intelligence (AI) offer attractive solutions to address the shortage of health care resources and improve the capacity of the local health care infrastructure. However, AI and ML should also be used cautiously, due to potential issues of fairness and algorithmic bias that may arise if not applied properly. Furthermore, populations in LMICs can be particularly vulnerable to bias and fairness in AI algorithms, due to a lack of technical capacity, existing social bias against minority groups, and a lack of legal protections. In order to address the need for better guidance within the context of global health, we describe three basic criteria (Appropriateness, Fairness, and Bias) that can be used to help evaluate the use of machine learning and AI systems: 1) APPROPRIATENESS is the process of deciding how the algorithm should be used in the local context, and properly matching the machine learning model to the target population; 2) BIAS is a systematic tendency in a model to favor one demographic group vs another, which can be mitigated but can lead to unfairness; and 3) FAIRNESS involves examining the impact on various demographic groups and choosing one of several mathematical definitions of group fairness that will adequately satisfy the desired set of legal, cultural, and ethical requirements. Finally, we illustrate how these principles can be applied using a case study of machine learning applied to the diagnosis and screening of pulmonary disease in Pune, India. We hope that these methods and principles can help guide researchers and organizations working in global health who are considering the use of machine learning and artificial intelligence.
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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.