Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece.
dc.contributor.author | Papadopoulou, Paraskevi | |
dc.contributor.author | Lytras, Miltiadis | |
dc.date.accessioned | 2024-05-13T06:20:10Z | |
dc.date.available | 2024-05-13T06:20:10Z | |
dc.date.issued | 2024-08-01 | |
dc.identifier.citation | Papadopoulou, P., & Lytras, M. (2024). Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece. In M. Lytras, A. Housawi, B. Alsaywid, & N. R. Aljohani (Eds.), Next generation eHealth: Applied data science, machine learning and extreme computational intelligence (pp. xx-xx). Elsevier. ISBN 9780443136191. | en_US |
dc.identifier.isbn | 9780443136191 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.14131/1635 | |
dc.description.abstract | The rapid advancement of Artificial Intelligence (AI) in healthcare presents challenges and opportunities for patient welfare. New policies, governance, and interoperability standards have been introduced necessitating patient and stakeholder engagement. The Organization for Economic Cooperation and Development (OECD) countries have prioritized health transformation through the use of health data, digital tools, and AI-driven healthcare innovation. AI has enormous potential to improve activities from research to treatment, administration, patient welfare, and value for money. However, responsible development is crucial to avoid misuse. This chapter explores the ethical considerations of AI-generated healthcare innovation, emphasizing the need for a balance between AI-driven progress and patient welfare. It examines progress in OECD countries, with a specific focus on enhancing patient welfare in Greece. Challenges related to treatment, data privacy, transparency, fairness, algorithmic bias, informed consent, and impact on healthcare professionals are discussed. Regulatory frameworks are considered as well as professional guidelines in the context of international collaboration. The chapter provides insights and recommendations for a responsible and patient-centric approach, prioritizing patient welfare while navigating ethical dimensions. | en_US |
dc.subject | Healthcare innovation | en_US |
dc.subject | OECD countries | en_US |
dc.subject | Health data | en_US |
dc.subject | Digital tools | en_US |
dc.subject | Personalized medicine | en_US |
dc.subject | Welfare | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.title | Enhancing patient welfare through responsible and AI-driven healthcare innovation: Progress made in OECD countries and the case of Greece. | en_US |
dc.source.booktitle | Next generation eHealth: Applied data science, machine learning and extreme computational intelligence | en_US |
dc.source.volume | Next Generation Technology Driven Personalized Medicine And Smart Healthcare; | |
dc.contributor.researcher | External Collaboration | en_US |
dc.contributor.lab | NA | en_US |
dc.subject.KSA | HEALTH | en_US |
dc.contributor.ugstudent | 0 | en_US |
dc.contributor.alumnae | 0 | en_US |
dc.source.index | Scopus | en_US |
dc.contributor.department | Computer Science | en_US |
dc.contributor.pgstudent | 0 | en_US |
dc.contributor.firstauthor | Papadopoulou, Paraskevi |