Protecting Informational Self-determination in AI-driven Precision Oncology: Privacy, Ethics, and Governance Challenges.

Artificial intelligence (AI) is rapidly transforming cancer care, from diagnosis and risk prediction to treatment planning and drug development. These advances depend on the large-scale integration of genomic, imaging, and clinical data, intensifying privacy and ethical concerns. This study examines emerging challenges in AI-driven precision oncology and explores strategies for protecting patients' informational self-determination through a narrative ethical and policy analysis informed by recent empirical and technical research. Even data considered de-identified, including molecular and imaging profiles, may enable inference of sensitive attributes through machine-learning analytics. Although many patients support the clinical promise of AI, they express concerns about secondary data use without meaningful consent, commercialization, and security breaches. Privacy violations threaten autonomy, confidentiality, and trust and may exacerbate existing inequities. Current regulatory frameworks, including the General Data Protection Regulation and the Health Insurance Portability and Accountability Act, focus primarily on identifiability and the removal of explicit identifiers. However, AI systems generate risks not only through re-identification but also through inferential analytics that derive new privacy-relevant information, such as disease susceptibility or familial risk, from ostensibly anonymized data. Addressing these gaps requires multilayered governance that combines privacy-preserving technologies, dynamic consent, data minimization, transparency, and strengthened legal safeguards to sustain public trust.