ReviewThe British journal of radiology2023
AI pitfalls and what not to do: mitigating bias in AI.
Review in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
What it found
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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.
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Who cites it
97 citing papers in PubMed.
- Cultural bias in large language models' ability to follow neuroradiology guidelines.European radiology · 2026Article
- Evaluating sex bias in artificial intelligence-based echocardiographic analysis: a retrospective study on individuals with normal cardiac function and patients with systolic and diastolic dysfunction.Cardiovascular ultrasound · 2026Article
- Review
- Subcategorisation of Data for AI Models in Healthcare: A Case Study in Mammography.Diagnostics (Basel, Switzerland) · 2026Article
- Patient Perceptions of Artificial Intelligence-Supported Shared Decision-Making in UK Primary Care for Multiple Long-Term Conditions: Qualitative Study.Journal of medical Internet research · 2026Article
- Regulating digital surgery to balance safety and innovation: a SAGES white paper.Surgical endoscopy · 2026Article
- From "high AUC" to evidentiary reliability: audit-ready deployment standards for deep-learning rib-fracture detection in postmortem CT.International journal of legal medicine · 2026Article
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Artificial intelligence based on forensic medical databases: construction, application, and prospects.Forensic science, medicine, and pathology · 2026Review
- Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.NPJ digital medicine · 2026Article
- Shift happens: a fairness-oriented framework for medical classification under hidden bias.International journal of computer assisted radiology and surgery · 2026Article
- Exploring Patient Perspectives on the Use of Artificial Intelligence to Inform Joint Decision-Making for Patients With Multiple Conditions in Primary Care in the United Kingdom: Qualitative Study.Journal of medical Internet research · 2026Article
- Multi-site distributed training with data protections for PET-based synthetic CT.EJNMMI physics · 2026Article
- Artificial intelligence in diagnostic imaging: collaborative asset or looming replacement?Oral radiology · 2026Review
- Important Ethical, Technical, and Epidemiological Considerations in an AI Tool Production (ETEPAI): Scoping Review.JMIR AI · 2026Review
- Findings from a transformer-based prediction model: social norms and confidentiality are associated with STIs risk in British men and women.BMC public health · 2026Article
- Autonomous artificial intelligence, scientific research, and human values.AI and ethics · 2026Article
- EHR Sampling Interval Bias Detection and Burden of Blood Pressure Excursions: Implications for Clinical Decision Support and Model Validity in Pediatric ECMO.Information (Basel) · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
37 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
9 authors.
Funding
Abstract
Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications increases, we are learning the failures of these models and how they can perpetuate bias. With these new lessons, we need to prioritize bias evaluation and mitigation for radiology applications; all the while not ignoring the impact of changes in the larger enterprise AI deployment which may have downstream impact on performance of AI models. In this paper, we provide an updated review of known pitfalls causing AI bias and discuss strategies for mitigating these biases within the context of AI deployment in the larger healthcare enterprise. We describe these pitfalls by framing them in the larger AI lifecycle from problem definition, data set selection and curation, model training and deployment emphasizing that bias exists across a spectrum and is a sequela of a combination of both human and machine factors.
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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.