ReviewTherapeutics and clinical risk management2026
Circulating Biomarkers with Artificial Intelligence for Clinical Risk Assessment and Therapeutic Monitoring: Evidence, Limitations, and Translation.
Review in Therapeutics and clinical risk management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Circulating biomarkers, particularly cell-free DNA (cfDNA), are transforming contemporary medicine through their capacity to provide dynamic, minimally invasive assessments of disease dynamics. Their clinical utility has broadened substantially to include oncology, cardiovascular conditions, transplant monitoring, infectious diseases, and persistent inflammatory disorders. Nevertheless, the increasing complexity and volume of biomarker-related data pose substantial challenges, necessitating the integration of artificial intelligence (AI) and machine learning methodologies for improved data interpretation, risk assessment, and clinical decision-making. This review presents a thorough examination of recent progress in the integration of circulating biomarkers with AI within therapeutic contexts and clinical risk management. It outlines the translational trajectory from molecular signatures to precision medicine, addresses present constraints, and explores prospective developments. The objective of this review is to serve as a systematic resource for both clinical practice and research, thereby promoting the evolution of personalized medicine through innovative, biomarker-driven strategies supported by AI.
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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.