Artificial Intelligence in Healthcare: Balancing Innovation, Ethics, and Human Rights Protection

Objective : to identify key ethical, legal and social challenges related to the use of artificial intelligence in healthcare; to develop recommendations for creating adaptive legal mechanisms that can ensure a balance between innovation, ethical regulation and the protection of fundamental human rights. Methods : a multidimensional methodological approach was implemented, integrating classical legal analysis methods with modern tools of comparative jurisprudence. The study covers both the fundamental legal regulation of digital technologies in the medical field and the in-depth analysis of the ethical, legal and social implications of using artificial intelligence in healthcare. Such an integrated approach provides a comprehensive understanding of the issues and well-grounded conclusions about the development prospects in this area. Results : has revealed a number of serious problems related to the use of artificial intelligence in healthcare. These include data bias, nontransparent complex algorithms, and privacy violation risks. These problems can undermine public confidence in artificial intelligence technologies and exacerbate inequalities in access to health services. The authors conclude that the integration of artificial intelligence into healthcare should take into account fundamental rights, such as data protection and non-discrimination, and comply with ethical standards. Scientific novelty : the work proposes effective mechanisms to reduce risks and maximize the potential of artificial intelligence under crises. Special attention is paid to regulatory measures, such as the impact assessment provided for by the Artificial Intelligence Act. These measures play a key role in identifying and minimizing the risks associated with high-risk artificial intelligence systems, ensuring compliance with ethical standards and protection of fundamental rights. Practical significance : adaptive legal mechanisms were developed, that support democratic norms and respond promptly to emerging challenges in public healthcare. The proposed mechanisms allow achieving a balance between using artificial intelligence for crisis management and human rights. This helps to build confidence in artificial intelligence systems and their sustained positive impact on public healthcare.

Authors

Institutions

Publication Details

Journal
Journal of Digital Technologies and Law
Published
2025-03-27
DOI
https://doi.org/10.21202/jdtl.2025.7
Citations
2
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence in Healthcare: Balancing Innovation, Ethics, and Human Rights Protection

Susana Antas Videira, Ricardo Pedro, Pedro Miguel Alves Ribeiro Correia
2 citations
Journal of Digital Technologies and Law
Artificial Intelligence in Healthcare and Education
1.31
article

Artificial Intelligence in Healthcare: Balancing Innovation, Ethics, and Human Rights Protection

Susana Antas Videira, Ricardo Pedro, Pedro Miguel Alves Ribeiro Correia
article en
2 citations

Abstract

Objective : to identify key ethical, legal and social challenges related to the use of artificial intelligence in healthcare; to develop recommendations for creating adaptive legal mechanisms that can ensure a balance between innovation, ethical regulation and the protection of fundamental human rights. Methods : a multidimensional methodological approach was implemented, integrating classical legal analysis methods with modern tools of comparative jurisprudence. The study covers both the fundamental legal regulation of digital technologies in the medical field and the in-depth analysis of the ethical, legal and social implications of using artificial intelligence in healthcare. Such an integrated approach provides a comprehensive understanding of the issues and well-grounded conclusions about the development prospects in this area. Results : has revealed a number of serious problems related to the use of artificial intelligence in healthcare. These include data bias, nontransparent complex algorithms, and privacy violation risks. These problems can undermine public confidence in artificial intelligence technologies and exacerbate inequalities in access to health services. The authors conclude that the integration of artificial intelligence into healthcare should take into account fundamental rights, such as data protection and non-discrimination, and comply with ethical standards. Scientific novelty : the work proposes effective mechanisms to reduce risks and maximize the potential of artificial intelligence under crises. Special attention is paid to regulatory measures, such as the impact assessment provided for by the Artificial Intelligence Act. These measures play a key role in identifying and minimizing the risks associated with high-risk artificial intelligence systems, ensuring compliance with ethical standards and protection of fundamental rights. Practical significance : adaptive legal mechanisms were developed, that support democratic norms and respond promptly to emerging challenges in public healthcare. The proposed mechanisms allow achieving a balance between using artificial intelligence for crisis management and human rights. This helps to build confidence in artificial intelligence systems and their sustained positive impact on public healthcare.

Journal of Digital Technologies and LawVol. 3(1)
University of Lisbon (PT), University of Coimbra (PT), EU Business School, Geneva (CH)
Openalex Percentile: Top 17%
Artificial Intelligence in Healthcare and Education
1.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.