ReviewAnnals of laboratory medicine2025
Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.
Review in Annals of laboratory medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Essential Data Elements for Laboratory Test Interoperability: An Expert Consensus-Based Framework.Annals of laboratory medicine · 2026Guideline
- Ethical guidelines for deploying artificial intelligence applications in the pathology field: Lessons learned from a prospective framework in a large tertiary care academic medical center.Journal of pathology informatics · 2026Article
- Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.Journal of medical microbiology · 2026Review
- Enhancing Glomerular Hematuria Identification in Automated Urinalysis Using a Light Gradient Boosting Machine-Based Model: A Diagnostic Accuracy Study.Annals of laboratory medicine · 2026Article
- New paradigm of q-CAR drug development targeting disease-specific protein conformations.npj drug discovery · 2026Review
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
- Enhancing Clinical Cardiac Care: Predicting In-Hospital Cardiac Arrest With Machine Learning.Annals of laboratory medicine · 2025Article
- Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.Journal of cellular and molecular medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital data in healthcare, laboratory results can strongly influence AI and ML algorithms that require large sets of healthcare data for training. Embedded bias introduced into AI and ML models not only has disastrous consequences for quality of care but also may perpetuate and exacerbate health disparities. The lack of test harmonization, which is defined as the ability to produce comparable results and the same interpretation irrespective of the method or instrument platform used to produce the result, may introduce aggregation bias into algorithms with potential adverse outcomes for patients. Limited interoperability of laboratory results at the technical, syntactic, semantic, and organizational levels is a source of embedded bias that limits the accuracy and generalizability of algorithmic models. Population-specific issues, such as inadequate representation in clinical trials and inaccurate race attribution, not only affect the interpretation of laboratory results but also may perpetuate erroneous conclusions based on AI and ML models in the healthcare literature.
Indexed as
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.