Artificial intelligence, biomarker discovery, clinical trials, machine learning, precision medicine, patient stratification, clinical research optimization

Biomarkers are measurable biological characteristics that can support disease diagnosis, prognosis, treatment selection, and monitoring oftherapeutic response. Their discovery increasingly depends on high-dimensional and heterogeneous data such as genomics, transcriptomics,proteomics, medical imaging, electronic health records, and wearable-device measurements. Artificial intelligence (AI) can help identifypatterns across these sources and support more efficient clinical-trial planning, including patient stratification, eligibility screening, recruitmentforecasting, endpoint assessment, and risk monitoring. This paper presents a conceptual framework connecting AI-enabled biomarker discoverywith clinical-trial optimization. The framework covers data governance, preprocessing, multimodal feature integration, candidate selection,validation, trial feasibility, and continuous monitoring. It emphasizes that computational performance alone does not establish clinicalusefulness: candidate biomarkers require independent replication, analytical validation, clinical validation, and evidence of utility in a clearlydefined context of use. The paper synthesizes published literature and regulatory principles; it does not report a newly conducted clinical studyor claim original performance results. Responsible implementation requires privacy protection, representative data, transparent documentation,clinician oversight, and prospective evaluation before AI outputs influence patient care.