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Peer-reviewedCC BY-NC 4.0Therapeutics and Clinical Risk Management

Circulating Biomarkers with Artificial Intelligence for Clinical Risk Assessment and Therapeutic Monitoring: Evidence, Limitations, and Translation

  • Wenyuan Wei1
  • Rui Zhang1
  • Zhiying Zhang1
  1. 1Department of Thyroid Breast Surgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010050, People’s Republic of China
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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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Cite

BibTeXRIS
BibTeX
@article{wei2026circulating,
  title = {{Circulating Biomarkers with Artificial Intelligence for Clinical Risk Assessment and Therapeutic Monitoring: Evidence, Limitations, and Translation}},
  author = {Wenyuan Wei and Rui Zhang and Zhiying Zhang},
  journal = {Therapeutics and Clinical Risk Management},
  year = {2026},
  month = oct,
  doi = {10.2147/tcrm.s612081},
  url = {https://doi.org/10.2147/tcrm.s612081},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Circulating Biomarkers with Artificial Intelligence for Clinical Risk Assessment and Therapeutic Monitoring: Evidence, Limitations, and Translation
AU  - Wenyuan Wei
AU  - Rui Zhang
AU  - Zhiying Zhang
PY  - 2026
DA  - 2026/10/27
JO  - Therapeutics and Clinical Risk Management
DO  - 10.2147/tcrm.s612081
UR  - https://doi.org/10.2147/tcrm.s612081
AB  - 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.
SN  - 1176-6336
SN  - 1178-203X
ER  -