# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 4 Ethical Considerations in AI-Driven PM  
This data-driven approach raises critical concerns regarding patient privacy, especially when handling sensitive medical and genetic information. Given the high-stakes nature of PM, where personal health data are involved, robust solutions are required to ensure data protection without compromising the utility of AI in healthcare [55]. Privacy concerns are compounded by the vast amounts of data used by AI systems, such as electronic health records (EHRs), genomic data, and imaging. Ensuring compliance with privacy regulations, such as GDPR and HIPAA, while maintaining AI performance requires the adoption of advanced data security mechanisms such as encryption, anonymization, and access control. This limitation has driven a growing focus on privacy-preserving approaches, including Federated Learning (FL), blockchain technology, and generative adversarial networks [56]. FL, introduced by Google in 2016 [57], allows decentralized data analysis by enabling multiple institutions to collaboratively train UL models without centralizing the raw data. By sharing only model updates instead of patient data, FL enhances privacy and maintains the security of sensitive medical information. This approach ensures that UL can still leverage large-scale datasets to reveal crucial insights in PM while complying with privacy regulations like GDPR. To further strengthen privacy protections, additional mechanisms such as differential privacy and cryptographic methods (including homomorphic encryp  
Figure 5: TM vs. PM. In the left image, a group of people, with different lifestyles but sharing the same pathology, can be observed. The approach of TM is to group the population under examination based on pathology and the provision of the same therapy for the entire population. In contrast, PM examines common lifestyles and genetic and/or environmental factors, identifying subgroups of populations, represented by the different circles, and confers a different therapy for each subgroup. tion and secure multi-party computation) are being integrated into newer FL models [58]. These techniques, when integrated with UL, form a robust framework for advancing PM. By protecting patient data while allowing for meaningful analysis, they address ethical concerns surrounding privacy and security. Moreover, cybersecurity measures, such as Secure Enclave technologies, should be implemented to safeguard data integrity and protect against cyber threats, which are increasingly pertinent as AI becomes more prevalent in healthcare. Another key consideration is transparency, as the complexity of UL models can obscure decision-making processes, raising skepticism among healthcare professionals and patients. Establishing guidelines for transparent reporting of UL models and their decision-making processes can build trust among clinicians and patients, ensuring that AI-generated insights are not only accurate but also interpretable and reliable. Bias in AI algorithms represents another significant ethical challenge [59]. If training datasets are not representative of diverse populations, the resulting model may inadvertently spread existing healthcare disparities. This could lead to unequal access to AI-driven treatments, and already marginalized groups may not benefit from the advancements in PM. So, mitigating bias in AI models necessitates diverse training datasets, fairness-aware development techniques [60], regular auditing, and ongoing evaluations to ensure equitable clinical applications. Finally, the integration of AI into PM, while offering promising prospects for personalizing care and accelerating research, raises important questions about professional responsibility and patient safety. Physicians must learn to view AI-generated recommendations as tools to support their clinical judgment and avoid delegating crucial decisions such as diagnosis or choice of therapy to these technologies [61].