Philosophical Ethics of AI in Healthcare: A Case Study of NovaCure Hospital

Thesis Abstract

This study investigates the philosophical ethics surrounding the deployment of artificial intelligence in healthcare through a focused case study of NovaCure Hospital, addressing the problem of how AI-driven decisions interface with core ethical principles such as autonomy, beneficence, non-maleficence, justice, and patient privacy amidst real-world clinical workflows. The aim is to illuminate normative tensions, accountability mechanisms, and governance structures that shape ethically sound AI adoption, with specific objectives (1) to analyze how NovaCure’s AI systems influence informed consent processes and patient autonomy; (2) to examine fairness and bias in algorithmic triage and resource allocation; (3) to evaluate transparency and explainability requirements in clinician-patient interactions; (4) to assess data privacy, consent, and data stewardship practices; and (5) to develop an integrated ethical framework applicable to hospital AI governance. The study adopts a multi-method design combining qualitative and quantitative strands. The population comprises clinical staff (n=60), including physicians, nurses, and data stewards, and a patient cohort (n=200) treated with AI-assisted interventions over the previous two years. A purposive sample of 20 clinical leaders and 15 patients will be interviewed, complemented by a survey of 120 clinicians and 80 patients to triangulate perspectives. Data collection instruments include semi-structured interviews guided by principlism and virtue ethics frameworks, a validated patient experience survey, and hospital audit checklists addressing consent protocols, data governance, and disclosure practices. For analysis, qualitative data will be subjected to thematic analysis following Braun and Clarke’s approach, with inter-coder reliability checks (Cohen’s kappa ? 0.75). Quantitative data will be analyzed using regression analysis to test associations between AI-driven decision transparency and patient trust, as well as ANOVA to examine differences across professional roles. A policy and governance lens will inform a conceptual model integrating principled ethics, accountability, and clinical pragmatism. The study also employs a multi-criteria decision analysis to contrast normative expectations with practical constraints in NovaCure’s AI deployment. Expected findings include (a) evidence that higher explainability correlates with improved patient trust and adherence, (b) identification of demographic and clinical factors associated with algorithmic bias in triage decisions, (c) gaps between formal consent procedures and actual patient understanding, and (d) a mapping of governance gaps in data stewardship, incident reporting, and accountability attribution. The anticipated contribution to knowledge rests on advancing an empirically grounded ethical framework for AI in healthcare that integrates principled theories (deontological ethics, consequentialism, and virtue ethics) with organizational governance, offering transferable insights for similar institutions facing AI integration. The study aims to yield concrete recommendations for NovaCure Hospital, including standardized consent dialogues for AI-enabled interventions, bias mitigation protocols in model development and auditing, transparency dashboards for patients and clinicians, enhanced data governance and privacy impact assessments, and an integrated ethics oversight committee with role-specific mandates. The conclusion will articulate a balanced account of AI’s potential to improve clinical outcomes while safeguarding core ethical values, emphasizing the necessity of continuous ethical reflexivity, iterative governance, and stakeholder engagement. Recommendations will target hospital leadership, clinical teams, data science units, and policy-makers, urging the adoption of a dynamic governance framework that accommodates evolving AI capabilities, compliance with national data protection standards, and ongoing training in ethical AI for healthcare professionals.