Biomarkers are essential for the rational development of medical diagnostics and therapeutics, but confusion remains about their definitions and applications in research and clinical practice. The FDA and NIH have established definitions for different biomarker classes, placing them in the context of their use in patient care, clinical research, or therapeutic development. Complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies are reshaping diagnostic and therapeutic technologies. A collaborative, interdisciplinary approach to biomarker development is needed to ensure quality and reproducibility, leading to evidence-based biomarker development that meets scientific and clinical needs. Biomarkers are defined as characteristics measured as indicators of normal biological processes, pathogenic processes, or responses to exposure or intervention. They differ from clinical outcome assessments (COAs), which measure outcomes directly important to patients. Biomarkers serve various purposes, including linking measurements to predictions of COAs. Validation is crucial for biomarkers to be used in regulatory approval, reimbursement, or clinical use. Diagnostic biomarkers detect or confirm the presence of a disease, while monitoring biomarkers assess the status of a disease or medical condition. Pharmacodynamic/response biomarkers indicate the effect of a medical product or environmental agent. Predictive biomarkers predict the likelihood of a favorable or unfavorable response to treatment. Prognostic biomarkers identify the likelihood of a clinical event or disease progression. Safety biomarkers indicate the likelihood, presence, or extent of toxicity. The distinction between prognostic and predictive biomarkers is critical. Prognostic biomarkers are associated with differential disease outcomes, while predictive biomarkers identify factors associated with treatment response. Surrogates, which are used to substitute for clinical outcomes, must be validated to ensure they accurately reflect clinical outcomes. The field of biomarkers is evolving with the development of complex biomarkers and digital biomarkers. These advancements, along with improvements in systems biology and computational power, are transforming the understanding of biological processes and clinical outcomes. Collaborative regulatory science is essential to ensure that biomarker development keeps pace with scientific and clinical needs. The importance of reproducibility and quality in biomarker research cannot be overstated, as it ensures the reliability and validity of biomarker applications in healthcare.Biomarkers are essential for the rational development of medical diagnostics and therapeutics, but confusion remains about their definitions and applications in research and clinical practice. The FDA and NIH have established definitions for different biomarker classes, placing them in the context of their use in patient care, clinical research, or therapeutic development. Complex composite biomarkers and digital biomarkers derived from sensors and mobile technologies are reshaping diagnostic and therapeutic technologies. A collaborative, interdisciplinary approach to biomarker development is needed to ensure quality and reproducibility, leading to evidence-based biomarker development that meets scientific and clinical needs. Biomarkers are defined as characteristics measured as indicators of normal biological processes, pathogenic processes, or responses to exposure or intervention. They differ from clinical outcome assessments (COAs), which measure outcomes directly important to patients. Biomarkers serve various purposes, including linking measurements to predictions of COAs. Validation is crucial for biomarkers to be used in regulatory approval, reimbursement, or clinical use. Diagnostic biomarkers detect or confirm the presence of a disease, while monitoring biomarkers assess the status of a disease or medical condition. Pharmacodynamic/response biomarkers indicate the effect of a medical product or environmental agent. Predictive biomarkers predict the likelihood of a favorable or unfavorable response to treatment. Prognostic biomarkers identify the likelihood of a clinical event or disease progression. Safety biomarkers indicate the likelihood, presence, or extent of toxicity. The distinction between prognostic and predictive biomarkers is critical. Prognostic biomarkers are associated with differential disease outcomes, while predictive biomarkers identify factors associated with treatment response. Surrogates, which are used to substitute for clinical outcomes, must be validated to ensure they accurately reflect clinical outcomes. The field of biomarkers is evolving with the development of complex biomarkers and digital biomarkers. These advancements, along with improvements in systems biology and computational power, are transforming the understanding of biological processes and clinical outcomes. Collaborative regulatory science is essential to ensure that biomarker development keeps pace with scientific and clinical needs. The importance of reproducibility and quality in biomarker research cannot be overstated, as it ensures the reliability and validity of biomarker applications in healthcare.