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Artificial Intelligence for the Future of Medicine

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Artificial Intelligence and Machine Learning for Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 229))

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Abstract

Since its origins, medicine has been more linked to the cure of diseases than to their prevention. This is due to multiple factors, training of health professionals aimed at curing diseases, lack of quality data, processing capacity, poor multidisciplinary approach, etc. However, this paradigm is changing, focusing on maintaining the health of individuals to avoid diseases, improving social welfare. To achieve this, the new approach proposes that medicine must be Preventive, Participatory, Predictive, and Personalized (P4 Medicine). In this chapter, we will analyze how artificial intelligence can convincingly contribute to the construction of P4 Medicine, through the processing of key data such as DNA, electronic medical records and environmental variables to which people have been exposed. Here we can find complex data such as Computed Tomography images, electroencephalograms, free text in electronic medical records, pharmacological data, etc. These data have grown exponentially and efforts to improve their quality are already paying off. However, it is no longer possible for a health professional to analyze them to provide a better diagnosis or carry out preventive work on diseases, requiring the formation of multidisciplinary teams to find new solutions to ancient problems, such as healthcare, where data processing, knowledge extraction and its subsequent parameterization in support systems for medical decision-making are vital to save lives. In this sense, artificial intelligence, together with new methods for processing complex data and computational resources to process massive data, will be key to improving the humanity health.

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Acknowledgements

The authors gratefully acknowledge financial support from ANID PIA/APOYO AFB180003. This work was supported by projects ANID (National Research and Development Agency of Chile) Doctorado Nacional #2116137

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Authors and Affiliations

  1. Department of Industrial Engineering, Web Intelligence Consortium, Chile Research Centre, University of Chile, Av. Beauchef 851, East Tower, Office 619, 8370456, Santiago, Chile

    Rocío B. Ruiz & Juan D. Velásquez

  2. Instituto Sistemas Complejos de Ingeniería (ISCI), Santiago, Chile

    Rocío B. Ruiz & Juan D. Velásquez

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  1. Rocío B. Ruiz
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  2. Juan D. Velásquez
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Correspondence to Juan D. Velásquez .

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  1. Deakin University, Victoria, VIC, Australia

    Chee Peng Lim

  2. Royal Adelaide Hospital,, Adelaide, SA, Australia

    Ashlesha Vaidya

  3. College of Information Science and Eng, Ritsumeikan University, Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  4. ASF Insignia, Kyndryl Solutions Pvt Ltd,, Gurugram, Haryana, India

    Vaishnavi Jain

  5. Liverpool Hope University, KES International United Kingdom, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Ruiz, R.B., Velásquez, J.D. (2023). Artificial Intelligence for the Future of Medicine. In: Lim, C.P., Vaidya, A., Chen, YW., Jain, V., Jain, L.C. (eds) Artificial Intelligence and Machine Learning for Healthcare. Intelligent Systems Reference Library, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-031-11170-9_1

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