Machine learning, healthcare resource allocation, and patient consent

Area of Science:

• Bioethics
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• The ethical implications of machine learning (ML) in healthcare are increasingly scrutinized, particularly concerning patient informed consent.
• Existing bioethical discussions primarily focus on ML in diagnostics and treatment recommendations, neglecting its role in resource allocation.
• The impact of ML on patient consent within healthcare resource allocation remains underexplored.

Purpose of the Study:

• To identify the relevance of patient consent in healthcare resource allocation.
• To analyze how black box ML algorithms affect informed consent in resource allocation.
• To explore the ethical considerations and challenges of using ML in healthcare resource allocation.

Main Methods:

• Literature review of bioethics and health informatics research.
• Conceptual analysis of informed consent principles in the context of ML.
• Examination of opacity in ML algorithms and its effect on patient understanding and consent.

Main Results:

• Patient consent is relevant at multiple points in healthcare resource allocation.
• Black box ML algorithms introduce significant challenges to obtaining truly informed consent.
• Opacity in ML systems can act as a substantial barrier to patient consent and trust.

Conclusions:

• Ethical frameworks must adapt to address ML in healthcare resource allocation.
• Transparency and explainability in ML are vital for upholding patient autonomy.
• Further research is needed to develop practical guidelines for consent in AI-driven healthcare resource allocation.