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Leveraging Machine Unlearning for Better Medical Care and Data Protection in Healthcare 6.0
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Author(s): Santosh Moses (IQVIA, India), Konrad Obermann (M4Health, Germany), Kanishak Gautam (IQVIA, India), Gaurav Upadhyay (Delhi Technological University, India)and Jayathra Datla (IQVIA, India)
Copyright: 2025
Pages: 26
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.ch011
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Abstract
Machine Learning (ML), a key component of modern technology, evolved from Alan Turing's 1950s concepts and the Turing Test. Introduced in 1959, ML enables computers to perform tasks by learning from data rather than explicit programming. It includes supervised, unsupervised, semi-supervised, and reinforcement learning. As ML integrated into various industries, Machine Unlearning (MU) emerged to address data privacy and security. MU involves removing specific data points' influence from a trained model without complete retraining, ensuring sensitive information is efficiently erased while maintaining model integrity.
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