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Artificial Intelligence and Machine Learning in Clinical Research and Patient Remediation

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Artificial Intelligence and Machine Learning in Healthcare

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Abstract

With a significant increase in the amount of data generated in healthcare and associated research activities, researchers need an effective, efficient, and novel approach to store, manage, and analyze the collected data. Artificial intelligence (AI) and Machine learning (ML) are the new technologies that have emerged to serve healthcare data-related complexity and innovations efficiently. While AI is an application of computational algorithms to segregate, classify, analyze, and draw conclusions from a large set of data, ML is a subset of AI, which refers to the process of building statistical models to predict the outcomes or results from the given data. AI and ML techniques find applications, where the data is generated regularly and at any instance, is very large and complex for any human to process it. Hence, large-scale automation would help in deriving a correct inference thereby saving cost and time. Recent developments have shown that AI and ML have a comprehensive role in the future of healthcare research. The key areas of healthcare applications involve image analysis and diagnosis, recommendation of treatment, genome sequencing, statistical analysis of drugs, and similar administrative activities. These applications of AI and ML in the healthcare and medical fields possess unique challenges related to interpretation, performance and reliability. Therefore, in the chapter, we will cover the AI and ML techniques employed in image analysis and treatment recommendation, prediction of deceases, conducting drugs clinical trials and healthcare administration. We will also learn about the various challenges related to AI and ML in the healthcare and medical fields.

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Authors and Affiliations

  1. Renewable Energy Laboratory, School of Physical Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Monu Mishra & Manish K. Kashyap

  2. Department of Physics, Dyal Singh College, University of Delhi, New Delhi, 110003, India

    Monu Mishra

  3. Fraunhofer ENAS, Technologie-Campus 3, 09126, Chemnitz, Germany

    Vikas Dubey

  4. Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588-0664, USA

    Timothy A. Hackett

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  4. Manish K. Kashyap
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Editors and Affiliations

  1. Department of Statistics & Demography, National Institute of Health and Family Welfare, New Delhi, Delhi, India

    Dharmendra Kumar Yadav

  2. Center for Studies in Science Policy, Jawaharlal Nehru University, New Delhi, Delhi, India

    Anamika Gulati

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Mishra, M., Dubey, V., Hackett, T.A., Kashyap, M.K. (2023). Artificial Intelligence and Machine Learning in Clinical Research and Patient Remediation. In: Yadav, D.K., Gulati, A. (eds) Artificial Intelligence and Machine Learning in Healthcare. Springer, Singapore. https://doi.org/10.1007/978-981-99-6472-7_3

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