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Deep Learning for the Intersection of Ethics and Privacy in Healthcare
Abstract
The integration of deep learning in healthcare holds tremendous promise for improving patient care and medical research. However, this transformation comes with ethical considerations and privacy challenges that demand careful examination. An effort is made to explore fundamental ethical principles, data privacy issues, and the impact of bias and fairness on healthcare AI. It scrutinizes the critical need for informed consent, patient rights, and adherence to regulatory frameworks. The work established in this chapter highlights transparency and explainability as essential aspects of responsible AI deployment in healthcare services. Furthermore, the chapter also offers additional information on ethical decision-making frameworks, mechanisms for accountability, and auditing in deep learning projects. Case studies and real-world examples illustrate these concepts, guiding practitioners and researchers in their quest to navigate the intricate intersection of ethics and privacy in healthcare deep learning.
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