{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Thyroid Breast Surgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010050, People’s Republic of China"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wenyuan Wei"
    },
    {
      "affs": [
        0
      ],
      "name": "Rui Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhiying Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8aqy44vf8dqqw.bib",
  "cited_by": 0,
  "doi": "10.2147/tcrm.s612081",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13723182/",
  "id": "8aqy44vf8dqqw",
  "issns": [
    "1176-6336",
    "1178-203X"
  ],
  "landing_url": "https://doi.org/10.2147/tcrm.s612081",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Review"
    ],
    [
      "openalex",
      "1306|Cancer Genomics and Diagnostics"
    ]
  ],
  "openalex": "W7223931434",
  "pdf_url": "https://www.dovepress.com/article/download/121445",
  "pmcid": "PMC13723182",
  "pmid": "42967126",
  "published": "2026-10-27",
  "publisher": "Dove Press",
  "ris": "https://research.draconitic.com/paper/8aqy44vf8dqqw.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:21:49Z",
      "native_id": "PMC13723182",
      "source": "pmc",
      "url": "https://doi.org/10.2147/tcrm.s612081"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:1306|Cancer Genomics and Diagnostics",
      "subfield": "oncology"
    }
  ],
  "title": "Circulating Biomarkers with Artificial Intelligence for Clinical Risk Assessment and Therapeutic Monitoring: Evidence, Limitations, and Translation",
  "title_text": "Circulating Biomarkers with Artificial Intelligence for Clinical Risk Assessment and Therapeutic Monitoring: Evidence, Limitations, and Translation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T12:21:49Z",
  "url": "https://research.draconitic.com/paper/8aqy44vf8dqqw",
  "venue": "Therapeutics and Clinical Risk Management",
  "versions": []
}