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Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications
Muhammad Nawaz Sharif Univ Engn & Technol, Dept Elect Engn, Multan 66000, Pakistan..
Punjab Tianjin Univ Technol, Dept Elect Engn Technol, Lahore 54770, Pakistan..
Natl Univ Comp & Emerging Sci NUCES, Dept Comp Sci, Islamabad 44000, Pakistan..
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 16233-16254Article in journal (Refereed) Published
Abstract [en]

The application of Machine Learning (ML) in healthcare has opened unprecedented avenues for predictive analytics, diagnostics, and personalized medicine. However, the sensitivity of healthcare data and the ethical dilemmas associated with automated decision-making necessitate a rigorous ethical framework. This review paper aims to provide a comprehensive overview of the existing ethical frameworks that guide ML in healthcare and evaluates their adequacy in ad-dressing ethical challenges. Specifically, this article offers an in-depth examination of prevailing ethical constructs that oversee healthcare ML, spotlighting pivotal concerns: data protection, in-formed assent, equity, and patient autonomy. Various analytical approaches including quantitative metrics, statistical methods for bias detection, and qualitative thematic analyses are applied to address these challenges. Insights are further enriched through case studies of Clinical Decision Support Systems, Remote Patient Monitoring, and Telemedicine Applications. Each case is evaluated against existing ethical frameworks to identify limitations and gaps. Based on our com-prehensive review and evaluation, we propose actionable recommendations for evolving ethical guidelines. The paper concludes by summarizing key findings and underscoring the urgent need for robust ethical frameworks to guide ML applications in sensitive healthcare environments. Future work should focus on the development and empirical validation of new ethical frameworks that can adapt to emerging technologies and ethical dilemmas in healthcare ML.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2024. Vol. 12, p. 16233-16254
Keywords [en]
Ethical frameworks, machine learning, healthcare applications, data privacy
National Category
Health Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-66038DOI: 10.1109/ACCESS.2023.3340884ISI: 001155944200001Scopus ID: 2-s2.0-85179835954OAI: oai:DiVA.org:mdh-66038DiVA, id: diva2:1837597
Available from: 2024-02-14 Created: 2024-02-14 Last updated: 2024-02-14Bibliographically approved

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Rehman, Atiq Ur

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