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Cited by 35Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIEthics and bioethics in healthcare

Ethical frameworks for machine learning in sensitive healthcare applications

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Javed, Haseeb, Muqeet, Hafiz Abdul, Javed, Tahir, Rehman, Atiq Ur, Sadiq, Rizwan·2024·IEEE Access
DOI 10.1109/access.2023.3340884
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Abstract

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.

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  • Models validated on biased datasets frequently exhibit reduced accuracy for underserved populations, such as rural communities or individuals with unique healthcare needs.

    “For instance, Sikstrom et al. and Haseeb et al., [46,51] reveal that models validated on biased datasets frequently exhibit reduced accuracy for underserved populations, such as rural communities or individuals with unique healthcare needs.”

    From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [46]

    Section 2.3 (Fairness and Bias; page unavailable) says models trained on demographically unrepresentative data may make less accurate predictions for underrepresented groups. It does not establish that this frequently occurs in models validated on biased datasets. Section 3.3.1 mentions rural healthcare settings, but not reduced model accuracy in rural communities or among people with unique healthcare needs. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Algorithms trained on datasets with a male bias may fail to detect gender-specific conditions or present inaccuracies in diagnosing diseases that exhibit gender differences in symptoms.

    “For instance, algorithms trained on datasets with a male bias may fail to detect gender-specific conditions or present inaccuracies in diagnosing diseases that exhibit gender differences in symptoms [13,45,46].”

    From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [46]

    Section 2.3, “Fairness and Bias” (page unavailable), says training data can disproportionately represent demographic groups, including by gender, and that this may make predictions less reliable for underrepresented groups or lead to incorrect diagnoses. It does not specifically discuss male-biased datasets, failure to detect gender-specific conditions, or diseases with gender-differing symptoms. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Javed, H., Muqeet, H. A., Javed, T., Rehman, A. U., & Sadiq, R.
Affiliations
  • Muhammad Nawaz Sharif University of Engineering & Technology (PK)
  • Punjab Tianjin University of Technology (PK)
  • National University of Computer and Emerging Sciences (PK)
  • Mälardalen University (SE)
  • Central Asian University (UZ)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024
Volume
12
Pages
16233-16254
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2023.3340884
OpenAlex
W4389459160
Cited by
35
References
0
Citations by year
2026: 9 · 2025: 14 · 2024: 12
FWCI
1.40
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • Diva portal (Dalarna University Library) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
Keywords
machine learning ethics, artificial intelligence in healthcare, ethical frameworks, data protection, informed consent, algorithmic fairness, algorithmic bias, patient autonomy, clinical decision support systems, remote patient monitoring, telemedicine, automated decision-making

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