RESUMEN
Abstract The article explores the significance of biomarkers in clinical research and the advantages of utilizing artificial intelligence (AI) and machine learning (ML) in the discovery process. Biomarkers provide a more comprehensive understanding of disease progression and response to therapy compared to traditional indicators. AI and ML offer a new approach to biomarker discovery, leveraging large amounts of data to identify patterns and optimize existing biomarkers. Additionally, the article touches on the emergence of digital biomarkers, which use technology to assess an individual's physiological and behavioural states, and the importance of properly processing omics and multi-omics data for efficient handling by computer systems. However, the article acknowledges the challenges posed by AI/ML in the identification of biomarkers, including potential biases in the data and the need for diversity in data representation. To address these challenges, the article suggests the importance of regulation and diversity in the development of AI/ML algorithms.