Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

Journal: Computer methods and programs in biomedicine
PMID: 38905988

Abstract

BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In healthcare, artificial intelligence (AI) holds promise for advancing disease risk assessment and treatment outcome prediction. However, machine learning (ML) evolution raises concerns about data privacy and biases, especially in sensitive healthcare applications. The objective is to develop and implement a responsible AI model for CD prediction that prioritize patient privacy, security, ensuring transparency, explainability, fairness, and ethical adherence in healthcare applications.

Authors

  • Mahbuba Ferdowsi
    Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman (UTAR), Kajang, Selangor 43200, Malaysia. Electronic address: mahbubaferdowsi@gmail.com.
  • Md Mahmudul Hasan
    Queensland University of Technology (QUT), Centre for Accident Research and Road Safety Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia. Electronic address: mahmudul.hasan.eee.kuet@gmail.com.
  • Wafa Habib
    Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya (UM), Kuala Lumpur 50603, Malaysia.

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