Journal of Preventive Medicine and Public Health

Perspective

Protecting Informational Self-determination in AI-driven Precision Oncology: Privacy, Ethics, and Governance Challenges: Minsoo Jung; J Prev Med Public Health. 2026;59(4):428-431. Published online April 27, 2026; DOI: https://doi.org/10.3961/jpmph.26.094

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Abstract Summary PDF: 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.; Summary

Korean summary
인공지능 기반 정밀종양학은 암 진단과 치료의 정확성을 향상시키는 동시에, 유전체·영상·임상정보의 대규모 활용으로 인해 개인정보 자기결정권(informational self-determination)에 새로운 윤리적·법적 도전을 제기하고 있다. 본 논문은 기존의 동의 중심 개인정보 보호체계의 한계를 분석하고, 개인정보보호 기술, 동적 동의, 데이터 최소화, 투명성, 법적 보호를 통합한 다층적 거버넌스가 환자의 자율성과 신뢰를 지속적으로 보장하기 위한 핵심 전략임을 제시한다.

Key Message
AI-driven precision oncology challenges conventional privacy frameworks because machine-learning systems can generate sensitive inferences from ostensibly de-identified genomic and clinical data. Protecting informational self-determination therefore requires moving beyond one-time informed consent toward multilayered governance integrating privacy-preserving technologies, dynamic consent, transparency, legal safeguards, and equitable oversight. Such a governance framework is essential for preserving patient autonomy, sustaining public trust, and ensuring that advances in precision oncology are achieved without compromising privacy or exacerbating health inequities.

Review

The Next Frontiers in Preventive and Personalized Healthcare: Artificial Intelligent-powered Solutions: Rasit Dinc, Nurittin Ardic; J Prev Med Public Health. 2025;58(5):441-452. Published online May 29, 2025; DOI: https://doi.org/10.3961/jpmph.25.080

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6 Web of Science
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Artificial intelligence (AI)-enabled technologies have the potential to significantly increase diagnostic accuracy, optimize treatment strategies, and improve patient outcomes. They are revolutionizing the field of preventive and personalized medicine by providing data-driven insights. AI is capable of analyzing large and complex datasets such as genomic, environmental, and lifestyle information much faster and more conveniently than traditional methods. Advanced algorithmic architectures in AI can predict disease risks, identify biomarkers, and tailor interventions to individual needs. The enabling role of AI in real-time monitoring, predictive analysis, and drug discovery demonstrates its transformative potential in healthcare. The role of AI in multi-omics integration, wearable technologies, and precision therapies promises to redefine global healthcare paradigms, making personalized medicine more accessible and effective. However, ethical concerns that need to be addressed to ensure fair and transparent implementation include data privacy, algorithmic bias, and regulatory gaps. This article examines the integration of AI technologies with personalized healthcare. The study also highlights the need for interdisciplinary collaboration to maximize the benefits of AI in preventive and personalized healthcare and overcome barriers.

Summary

Key Message
Artificial intelligence significantly accelerates preventive and personalized medicine by analyzing complex genomic, environmental, and lifestyle datasets to predict disease risks, identify biomarkers, and tailor interventions to individual needs. Through real-time monitoring, predictive analysis, and precision therapies, AI-enabled technologies play a critical role in increasing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. However, successful implementation requires addressing critical challenges such as data privacy concerns, algorithmic bias, regulatory gaps, and the need for interdisciplinary collaboration to provide equitable, transparent, and accessible AI-enabled healthcare solutions.

Citations

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Special Article

Introduction of Vaccinomics to Develop Personalized Vaccines in Light of Changes in the Usage of Hantaan Virus Vaccine (Hantavax^®) in Korea: Jong-Myon Bae; J Prev Med Public Health. 2019;52(5):277-280. Published online August 7, 2019; DOI: https://doi.org/10.3961/jpmph.19.018

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216 Download
8 Crossref

Abstract Summary PDF Supplementary Material

The Ministry of Food and Drug Safety of Korea made an official announcement in March 2018 that the total number of inoculations of Hantaan virus vaccine (Hantavax^®) would change from 3 to 4. Some aspects of this decision remain controversial. Based on the characteristics of Hantaan virus (HTNV) and its role in the pathogenesis of hemorrhagic fever with renal syndrome, it might be difficult to develop an effective and safe HTNV vaccine through the isolate-inactivate-inject paradigm. With the development of high-throughput ‘omics’ technologies in the 21st century, vaccinomics has been introduced. While the goal of vaccinomics is to develop equations to describe and predict the immune response, it could also serve as a tool for developing new vaccine candidates and individualized approaches to vaccinology. Thus, the possibility of applying the innovative field of vaccinomics to develop a more effective and safer HTNV vaccine should be considered.

Summary

Korean summary
신증후출혈열 (Hemorrhagic fever with renal syndrome, HFRS)를 예방하기 위한 백신들이 여러 가지로 개발 중에 있지만, 한탄 바이러스 (Hantaan virus) 의 병원체 특성과 HFRS의 임상적 특성으로 효과적이고 안전한 백신 개발을 어렵게 하고 있다. 투여할 백신에 대한 ‘면역과정을 이해하고 백신효능을 예측하여’ 백신개발에 활용하는 백신체학의 적용을 적극 강구할 필요가 있다.

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