ReviewDiagnostics (Basel, Switzerland)2024
Machine Learning for Patient-Based Real-Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Challenges in PT/INR harmonization across the total testing process.Biochemia medica · 2026Review
- Development and Evaluation of Artificial Intelligence-Based Two-Step Model for Automated Serum Quality Assessment in Clinical Laboratories.Annals of laboratory medicine · 2026Article
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- A Comparative Study of the Clinical Laboratory Quality Control Performance of AI-PBRTQC and Traditional PBRTQC Model in Tumor Marker Testing.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Comparison of EWMA, MA, and MQ Under a Unified PBRTQC Framework for Thyroid and Coagulation Tests.Diagnostics (Basel, Switzerland) · 2026Article
- AI Based Predictive Modelling for Internal Quality Control: A Machine Learning Approach Using Altair RapidMiner.EJIFCC · 2025Article
- Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.EJIFCC · 2025Article
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.
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.