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Artificial Intelligence in the Healthcare Sector

Author

Listed:
  • Julia M. Puaschunder

    (The New School, Department of Economics, USA)

  • Dieter Feierabend

    (Scientific Director NEOS Lab, Neubaugasse 64-66, Vienna, Austria)

Abstract

To an extent as never before in the history of medicine, computers are supporting human input, decision making and provision of data. In today’s healthcare sector and medical profession, AI, algorithms, robotics and big data are used to derive inferences for monitoring large-scale medical trends, detecting and measuring individual risks and chances based on data-driven estimations. A knowledge-intensive industry like the healthcare profession highly depends on data and analytics to improve therapies and practices. In recent years, there has been tremendous growth in the range of medical information collected, including clinical, genetic, behavioral and environmental data. Every day, healthcare professionals, biomedical researchers and patients produce vast amounts of data from an array of devices. These include electronic health records (EHRs), genome sequencing machines, high-resolution medical imaging, smartphone applications and ubiquitous sensing, as well as Internet of Things (IoT) devices that monitor patient health (OECD 2015). Through machine learning algorithms and unprecedented data storage and computational power, AI technologies have most advanced abilities to gain information, process it and give a well-de?ned output to the end-user. Daily monitoring thereby aids to create big data to recognize behavioral patterns’ relation to health status in order to create predictions with highest mathematical precision based on big data capturing large-scale samples. AI thereby enlightens to analyze the relation between prevention and treatment and patient outcomes in all stages of diagnosis, treatment, drug development and monitoring, personalized medicine, patient control and care. Advanced hospitals are looking into AI solutions to support and perform operational initiatives that increase precision and cost e?ectiveness. Robotics have been used for disabled and patient care assistance. Medical decision making has been supported through predictive analytics and general healthcare management technology. Network connectivity allows access to a?ordable healthcare around the globe in a cost-effective way.

Suggested Citation

  • Julia M. Puaschunder & Dieter Feierabend, 2019. "Artificial Intelligence in the Healthcare Sector," Scientia Moralitas Journal, Scientia Moralitas, Research Institute, vol. 4(2), pages 1-14, December.
    • Handle: RePEc:smo:journl:v:4:y:2019:i:2:p:1-14
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    References listed on IDEAS

    as
    1. Julia M. Puaschunder, 2019. "On Artificial Intelligence’s Razor’s Edge: On the Future of Democracy and Society in the Artificial Age," Proceedings of the 12th International RAIS Conference, April 3-4, 2019 5JP, Research Association for Interdisciplinary Studies.
    2. Julia M. Puaschunder, 2019. "Artificial Intelligence Market Disruption," Proceedings of the 13th International RAIS Conference, June 10-11, 2019 01 JP, Research Association for Interdisciplinary Studies.
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    Citations

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    Cited by:

    1. Julia M. Puaschunder, 2020. "Inequality in the 21st Century: Climate, Digital Skills and Access to Education," Scientia Moralitas Conference Proceedings 010jmp, Research Association for Interdisciplinary Studies.
    2. Julia M. Puaschunder, 2022. "Digital Inequality: A Research Agenda," RAIS Conference Proceedings 2022-2023 0201, Research Association for Interdisciplinary Studies.

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