ReviewDiagnostics (Basel, Switzerland)2025
From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies.
Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Risk Phenotyping Before Graft Implantation: FTIR Spectroscopy and Machine Learning for Complementary Risk Stratification in Kidney Transplantation.Medical sciences (Basel, Switzerland) · 2026Article
- Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection.Cancers · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over recent years, machine learning and artificial intelligence have become critical components of many cancer detection tests, in particular multi-omic tests such as spectroscopic liquid biopsies. The complexity and multi-variate nature of spectral datasets makes machine learning invaluable in uncovering patterns that enable robust differentiation of cancer signals. However, introducing any AI-enabled medical device into clinical practice is challenging due to the regulatory requirements needed to progress from fundamental research to clinical and patient use. This review explores some of the fundamental concerns in bringing spectroscopic liquid biopsies to the clinic, including the need for explainable artificial intelligence and diverse validation sets. Addressing these issues is essential to accelerate clinical uptake with the ultimate goal of improving patient survival and quality of life.
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.