Biomarkers, in the realm of AI, are quantifiable, measurable data points that offer crucial insights into the health or disease status of an individual. They are incorporated into AI algorithms to create predictive models and drive decision making processes in various fields, predominately healthcare. They enable AI technologies to detect early signs of disease, track disease progress, and even anticipate response to treatment. Biomarkers can range from proteins measured in the blood, to complex imaging characteristics, to genetic sequences, to behavioral patterns, all of which can be analyzed by AI to improve healthcare outcomes.
Historically, the concept of biomarkers has been used in clinical medicine and research for several decades. However, the integration of biomarkers into AI to forecast health outcomes is a relatively recent phenomenon that has come into prominence with the advent of Big Data in healthcare around the 2010s. Coupling AI with biomarkers is now recognized as a powerful tool in precision medicine.
Significant contributors to the development and utilization of biomarkers within AI include pioneers in the field of AI in healthcare, such as Google's DeepMind and IBM's Watson Health. These organizations, among others, are intensely focused on incorporating biomarker data into their AI models to transform patient care and outcomes, making biomarkers a fundamental piece of the modern AI landscape in healthcare.