Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future Directions

Rachel Bennett, Mehdi Hemmati, Rajagopal Ramesh and Talayeh Razzaghi ()
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Rachel Bennett: University of Oklahoma
Mehdi Hemmati: University of Oklahoma
Rajagopal Ramesh: University of Oklahoma Health Sciences Center
Talayeh Razzaghi: University of Oklahoma

A chapter in Dynamics of Disasters, 2024, pp 15-53 from Springer

Abstract: Abstract Conventional medical practices rely on population-derived guidelines, striving for optimal outcomes for the “average” patient through a so-called “one-size-fits-all” approach. Precision health, on the other hand, enhances health decision-making by considering individual characteristics such as genotype, environment, and lifestyle. An in-depth analysis of the roles played by artificial intelligence (AI) and machine learning (ML) in precision health, personalized care, and disease prevention contributes to a comprehensive understanding of the dynamic healthcare landscape. This chapter navigates the paradigm shift from traditional medical practices to the burgeoning field of precision health, grounded in AI and modern ML. We provide a comprehensive overview of the application of AI/ML in three precision health categories: disease screening and detection, disease monitoring and progression, and treatment selection and outcome prediction. While addressing challenges in data quality and fairness, this chapter discusses the diverse considerations of stakeholders in realizing the benefits of precision health. Delving into AI/ML techniques, this chapter addresses challenges posed by massive multimodal health data, ensuring model trustworthiness and fairness, and highlighting notable techniques. Furthermore, this chapter extends to AI/ML applications, addressing diverse stakeholders’ needs, and discusses challenges in the practical application of AI/ML in precision health.

Date: 2024
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DOI: 10.1007/978-3-031-74006-0_2

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