Abstract
Clinical care is gradually transiting from the standard approach of “signs and symptoms” toward a more targeted approach that considerably trusts biomedical data and the gained knowledge. The uniqueness of this concept is implied by “precision medicine,” which amalgamates contemporary computational methodologies such as artificial intelligence and big data analytics for achieving an informed decision, considering variability in patient’s clinical, omics, lifestyle, and environmental data. In precision medicine, artificial intelligence is being comprehensively used to design and enhance diagnosis pathway(s), therapeutic intervention(s), and prognosis. This has led to a rational achievement for the identification of risk factors for complex diseases such as cancer, by gauging variability in genes and their function in an environment. It is as well being used for the discovery of biomarkers, that can be applied for patient stratification based on probable disease risk, prognosis, and/or response to treatment. The advanced computational expertise using artificial intelligence for biological data analysis is also being used to speed up the drug discovery process of precision medicine. In this chapter, we discuss the role and challenges of artificial intelligence in the advancement of precision medicine, accompanied by case studies in biomarker and drug discovery processes.
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Santoshi, S., Sengupta, D. (2021). Artificial Intelligence in Precision Medicine: A Perspective in Biomarker and Drug Discovery. In: Saxena, A., Chandra, S. (eds) Artificial Intelligence and Machine Learning in Healthcare . Springer, Singapore. https://doi.org/10.1007/978-981-16-0811-7_4
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