ReviewBiochemia medica2025
Are we ready to integrate advanced artificial intelligence models in clinical laboratory?
Review in Biochemia medica, 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.
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Advanced molecular detection, bioinformatics, and artificial intelligence era in medical laboratory science education.Journal of microbiology & biology education · 2026Article
- Article
- Perspectives on emerging challenges and training needs for the Laboratory Medicine specialist of the future.Advances in laboratory medicine · 2026Article
- Article
- Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy.BMC medical informatics and decision making · 2025Article
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- Exosomal biomarkers in leukemia: translational potential and regulatory challenges for precision medicine applications.Frontiers in immunology · 2025Review
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 application of advanced artificial intelligence (AI) models and algorithms in clinical laboratories is a new inevitable stage of development of laboratory medicine, since in the future, diagnostic and prognostic panels specific to certain diseases will be created from a large amount of laboratory data. Thanks to machine learning (ML), it is possible to analyze a large amount of structured numerical data as well as unstructured digitized images in the field of hematology, cytology and histopathology. Numerous researches refer to the testing of ML models for the purpose of screening various diseases, detecting damage to organ systems, diagnosing malignant diseases, longitudinal monitoring of various biomarkers that would enable predicting the outcome of each patient's treatment. The main advantages of advanced AI in the clinical laboratory are: faster diagnosis using diagnostic and prognostic algorithms, individualization of treatment plans, personalized medicine, better patient treatment outcomes, easier and more precise longitudinal monitoring of biomarkers,
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.