Artificial Intelligence in Biomarker Discovery: Applications Across Medical Specialties

Abstract

Artificial intelligence has emerged as a transformative force in biomarker discovery, reshaping how clinicians and researchers identify, validate, and apply indicators of disease across a wide spectrum of medical specialties. Traditional biomarker research is often hindered by the sheer complexity and heterogeneity of biological data, making it difficult to detect subtle patterns that link molecular, cellular, imaging, and clinical features to disease outcomes. AI overcomes these barriers by applying advanced machine learning and deep learning algorithms capable of analyzing large, multi-modal datasets and uncovering relationships that may remain hidden using conventional approaches. In oncology, AI-driven biomarker discovery integrates genomic, transcriptomic, and radiomic data to identify predictive signatures that guide precision therapies and improve early detection of cancers. In cardiology, AI analyzes imaging and proteomic biomarkers to predict cardiovascular risk and monitor treatment responses, enabling more personalized interventions. Neurology benefits from AI models that process neuroimaging, genetic data, and digital phenotyping to uncover biomarkers for complex disorders such as Alzheimer’s disease and multiple sclerosis. Similarly, in autoimmune and infectious diseases, AI enables integration of immune profiling with clinical and molecular data, advancing the identification of biomarkers for early diagnosis, prognosis, and therapeutic monitoring. Beyond specialty-specific applications, AI supports a broader shift toward personalized medicine by identifying biomarker combinations that reflect individual patient profiles. While challenges such as data quality, interpretability, and regulatory validation remain, the integration of AI in biomarker discovery promises to accelerate innovation and improve healthcare outcomes across disciplines.