ReviewDiagnostics (Basel, Switzerland)2021
Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement.Critical reviews in clinical laboratory sciences · 2025Review
- Toward High-Quality Real-World Laboratory Data in the Era of Healthcare Big Data.Annals of laboratory medicine · 2025Review
- Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data.Cardiovascular diabetology · 2024Article
- Clinlabomics: leveraging clinical laboratory data by data mining strategies.BMC bioinformatics · 2022Review
- Big Data in Laboratory Medicine-FAIR Quality for AI?Diagnostics (Basel, Switzerland) · 2022Review
- Current Issues, Challenges, and Future Perspectives in Clinical Laboratory Medicine.Journal of clinical medicine · 2022Article
- Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.
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.