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

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Dynamics of Disasters

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 217))

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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.

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Authors and Affiliations

  1. School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA

    Rachel Bennett, Mehdi Hemmati & Talayeh Razzaghi

  2. Department of Pathology and Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA

    Rajagopal Ramesh

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    Ilias S. Kotsireas

  2. Isenberg School of Management, University of Massachusetts, Amherst, MA, USA

    Anna Nagurney

  3. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  4. Universitat der Bundeswehr Munchen, Neubiberg, Bayern, Germany

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    Chrysafis Vogiatzis

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Bennett, R., Hemmati, M., Ramesh, R., Razzaghi, T. (2024). Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future Directions. In: Kotsireas, I.S., Nagurney, A., Pardalos, P.M., Pickl, S.W., Vogiatzis, C. (eds) Dynamics of Disasters. Springer Optimization and Its Applications, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-031-74006-0_2

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