ArticleJournal of bioethical inquiry2022
Bias in algorithms of AI systems developed for COVID-19: A scoping review.
Article in Journal of bioethical inquiry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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
21 citing papers in PubMed.
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- AI in variant analysis: fast track to genetic diagnoses.Human genetics · 2026Review
- Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation.JAMIA open · 2026Article
- Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.World journal of radiology · 2025Review
- Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members.Journal of medical Internet research · 2025Article
- AI ethics for the everyday intensivist.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- A Pipeline for Automated Quality Control of Chest Radiographs.Radiology. Artificial intelligence · 2025Article
- Integrating enterprise risk management to address AI-related risks in healthcare: Strategies for effective risk mitigation and implementation.Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management · 2025Article
- Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.Circulation. Genomic and precision medicine · 2025Review
- Bias recognition and mitigation strategies in artificial intelligence healthcare applications.NPJ digital medicine · 2025Review
- Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review.Journal of medical Internet research · 2025Article
- A scoping review of reporting gaps in FDA-approved AI medical devices.NPJ digital medicine · 2024Article
- Artificial Intelligence in Infectious Disease Clinical Practice: An Overview of Gaps, Opportunities, and Limitations.Tropical medicine and infectious disease · 2024Review
- Automatic detection of problem-gambling signs from online texts using large language models.PLOS digital health · 2024Article
- May Artificial Intelligence take health and sustainability on a honeymoon? Towards green technologies for multidimensional health and environmental justice.Global bioethics = Problemi di bioetica · 2024Article
- Health Equity in Clinical Research Informatics.Yearbook of medical informatics · 2023Review
- Bias in artificial intelligence algorithms and recommendations for mitigation.PLOS digital health · 2023Review
- Patient-centered pharmacovigilance: priority actions from the inherited bleeding disorders community.Therapeutic advances in drug safety · 2023Review
- A step toward building a unified framework for managing AI bias.PeerJ. Computer science · 2023Article
- Artificial intelligence for telemedicine diabetic retinopathy screening: a review.Annals of medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.
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.