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1 - Mapping the Digital Healthcare Revolution

Published online by Cambridge University Press:  08 September 2022

By
Marcelo Corrales Compagnucci ,
Mark Fenwick ,
Michael Lowery Wilson ,
Nikolaus Forgó  and
Till Bärnighausen
Edited by
Marcelo Corrales Compagnucci ,
Michael Lowery Wilson ,
Mark Fenwick ,
Nikolaus Forgó  and
Till Bärnighausen
Marcelo Corrales Compagnucci
Affiliation:
University of Copenhagen
Michael Lowery Wilson
Affiliation:
University of Turku, Finland
Mark Fenwick
Affiliation:
Kyushu University, Japan
Nikolaus Forgó
Affiliation:
Universität Wien, Austria
Till Bärnighausen
Affiliation:
Universität Heidelberg
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Summary

This introductory chapter briefly outlines the main theme of this volume, namely, to review the new opportunities and risks of digital healthcare from various disciplinary perspectives. These perspectives include law, public policy, organisational studies, and applied ethics. Based on this interdisciplinary approach, we hope that effective strategies may arise to ensure that benefits of this ongoing revolution are deployed in a responsible and sustainable manner. The second part of the chapter comprises a brief review of the four parts and fourteen substantive chapters that make up this volume.

Keywords

AIeHealthprivacydata protectionconsentGDPRpersonalised medicineriskstrustethicsplatformshospitalsmedical datagenetic dataAI wearables
Type
Chapter
Information
AI in eHealth
Human Autonomy, Data Governance and Privacy in Healthcare
 , pp. 1 - 16
Publisher: Cambridge University Press
Print publication year: 2022

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References

Afinito, L, Empowering the Connected Physician in the E-Patient Era: How Physicians Empowerment on Digital Health Tools Can Improve Patient Empowerment and Boost Health(Care) Outcomes (Routledge 2019).Google Scholar
Agah, A, Medical Applications of Artificial Intelligence (CRC Press 2014).Google Scholar
Barfield, W and Pagallo, U (eds.), Research Handbook on the Law of Artificial Intelligence (Edward Elgar Publishing 2018).CrossRefGoogle Scholar
Brindha, G, ‘A New Approach for Changes in Health Care’ (2012) 12(12) Middle East Journal of Scientific Research, 1657–62.Google Scholar
Chowkwanyn, M, ‘“Precision” Public Health – Between Novelty and Hype’ (2018) 379(15) New England Journal of Medicine.Google Scholar
Dolley, S, ‘Big Data’s Role in Precision Public Health’ (2018) 6, Article 68 Frontiers in Public Health.CrossRefGoogle ScholarPubMed
Dunn, A and others, ‘Social Media Interventions for Precision Public Health: Promises and Risks’ (2018) 1 NPJ Digital Medicine, 47.Google Scholar
Gerke, S, Young, S, and Cohen, G, ‘Ethical and Legal Aspects of Ambient Intelligence in Hospitals’ (2020) JAMA.Google Scholar
Guidelines 03/2020 on the processing of data concerning health for the purpose of scientific research in the context of COVID-19, https://edpb.europa.eu/our-work-tools/our-documents/ohjeet/guidelines-032020-processing-data-concerning-health-purpose_en, accessed 20 January 2021.Google Scholar
Jayanthi, P and others, ‘Fourth Industrial Revolution: An Impact on Healthcare Industry’, in Ahram, Tareq (ed.), Advances in Artificial Intelligence, Software and Systems Engineering (Springer 2019) 58.Google Scholar
Louwerse, M and others, ‘Social Cues in Animated Conversational Agents’, (2005) 19(6) Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 693–704.Google Scholar
Panesar, A, Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Apress 2019).Google Scholar
Prosperi, M, ‘Big Data Hurdles in Precision Medicine and Precision Public Health’(2018) 18 BMC Medical Informatics and Decision Making, 139.Google Scholar
Wachter, R, ‘Why Diagnostic Errors Don’t Get Any Respect and What Can Be Done about Them’ (2017) Health Affairs.Google Scholar
Yang, G, ‘Office Operating Problem Scoring System Based on AI’, in Yang, Hui (ed.), Artificial Intelligence: Science and Technology, Proceedings of the 2016 International Conference (AIST 2016) (World Scientific 2017).Google Scholar

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