Evidence map›Paper›PMID 40096210›Full record

ReviewSensors (Basel, Switzerland)2025

Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.

Konstantinos Lazaros, Styliani Adam, Marios G Krokidis, Themis Exarchos, Panagiotis Vlamos, Aristidis G Vrahatis

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Review
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  7. Autophagy in Sensorineural Hearing Loss: Jekyll or Hyde?International journal of molecular sciences · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Konstantinos LazarosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Styliani AdamBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0002-3845-8709
Marios G KrokidisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Themis ExarchosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Panagiotis VlamosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0003-0053-7847
Aristidis G VrahatisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0003-1892-0000

Funding

This work was supported by the European Union-Next Generation EU, Greece 2.0 National Re-covery and Resilience Plan Flagship program TAEDR-0535850. TAEDR-0535850.
6 · The paper itself

Abstract

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and patient discomfort. Non-invasive biomarkers represent a transformative alternative, providing diagnostic precision through accessible biological samples or physiological data, including blood, saliva, breath, and wearable health metrics. They encompass molecular and imaging approaches, revealing genetic, epigenetic, and metabolic alterations associated with disease states. Furthermore, advances in breathomics and gut microbiome profiling further expand their diagnostic scope. Even with their strengths in terms of safety, cost-effectiveness, and accessibility, non-invasive biomarkers face challenges in achieving monitoring sensitivity and specificity comparable to traditional clinical approaches. Computational advancements, particularly in artificial intelligence and machine learning, are addressing these limitations by uncovering complex patterns in multi-modal datasets, enhancing diagnostic accuracy and facilitating personalized medicine. The present review integrates recent innovations, examines their clinical applications, highlights their limitations and provides a concise overview of the evolving role of non-invasive biomarkers in precision diagnostics, positioning them as a compelling choice for large-scale healthcare applications.

Indexed as

Big DataBiomarkersMachine LearningHumansPrecision MedicineBiomarkersbig databiomarkersdiagnosticsmachine learningnon-invasive approaches

Identifiers

PMID40096210
PMCPMC11902325

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.