TY - CHAP
T1 - Ethical Considerations in AI-Enabled Healthcare
AU - Mishra, Vinaytosh
AU - Lurie, Yotam
AU - Mark, Shlomo
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Integrating Artificial Intelligence (AI) in healthcare has revolutionized patient care and operational workflows, yet it introduces significant ethical considerations. This chapter explores the impact of AI on key ethical principles—beneficence, nonmaleficence, autonomy, and justice. AI systems, while enhancing patient outcomes and operational efficiency, must be designed to promote patient welfare, minimize harm, and ensure equitable access to care. However, algorithmic bias presents a critical challenge, often resulting from biased data and flawed algorithms, leading to disparities in diagnosis and treatment, particularly for marginalized communities. The importance of diverse training data is emphasized to mitigate bias and foster fair and inclusive AI systems. This chapter underscores the necessity of ethical frameworks in AI development and implementation, addressing privacy, security, and transparency issues to build trust and ensure responsible AI use in healthcare.
AB - Integrating Artificial Intelligence (AI) in healthcare has revolutionized patient care and operational workflows, yet it introduces significant ethical considerations. This chapter explores the impact of AI on key ethical principles—beneficence, nonmaleficence, autonomy, and justice. AI systems, while enhancing patient outcomes and operational efficiency, must be designed to promote patient welfare, minimize harm, and ensure equitable access to care. However, algorithmic bias presents a critical challenge, often resulting from biased data and flawed algorithms, leading to disparities in diagnosis and treatment, particularly for marginalized communities. The importance of diverse training data is emphasized to mitigate bias and foster fair and inclusive AI systems. This chapter underscores the necessity of ethical frameworks in AI development and implementation, addressing privacy, security, and transparency issues to build trust and ensure responsible AI use in healthcare.
KW - Algorithmic bias
KW - Ethical considerations
KW - Explainability and trust
KW - Healthcare AI ethics
UR - http://www.scopus.com/inward/record.url?scp=105001252784&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-80813-5_18
DO - 10.1007/978-3-031-80813-5_18
M3 - Chapter
T3 - Studies in Computational Intelligence
SP - 271
EP - 282
BT - Studies in Computational Intelligence
PB - Springer Science and Business Media Deutschland GmbH
ER -
