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Machine Unlearning Models for Medical Care and Health Data Privacy in Healthcare 6.0
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Author(s): Varun Kumar (Dure Technologies Private Limited, India)and Dipanjan Sujit Roy (Dure Technologies Private Limited, India)
Copyright: 2025
Pages: 30
Source title:
Exploration of Transformative Technologies in Healthcare 6.0
Source Author(s)/Editor(s): Piyush Kumar (IILM University, Gurugram, India), Pankaj Rahi (Institute of Health Management and Research, Bangalore, India), S.D. Gupta (Institute of Health Management and Research, Bangalore, India), Kirti Udayai (Max Healthcare Institute Limited, India)and Prashant Singh (Independent Researcher, India)
DOI: 10.4018/979-8-3693-7210-4.ch010
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Abstract
The integration of advanced technologies in healthcare has undergone significant transformations throughout the 20th and 21st centuries, culminating in the era of Healthcare 6.0. This evolution in healthcare, particularly the integration of artificial intelligence, has necessitated the implementation of concepts like “Machine Unlearning”. This is crucial for addressing critical issues like data privacy compliance, bias mitigation, and data security. By enabling the removal of specific data points from ML models, Machine Unlearning aligns with international data protection laws (HIPAA, GDPR, and DPDPA). Its application emphasizes regulations concerning the right to be forgotten, data minimization, and patient consent, which are essential for legal compliance and maintaining patient trust in healthcare. This chapter discusses Machine Unlearning approaches like Exact Unlearning and Approximate Unlearning, along with other advanced methods. Lastly, it discusses real-world case studies, and identifies the way forward for future development & research on 'Machine Unlearning in Healthcare'.
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