Harnessing LLMs in Healthcare: Balancing Innovation with Patient Privacy
Background
The integration of large language models (LLMs) in healthcare promises transformative potential by enhancing diagnostic accuracy, personalizing treatment plans, and streamlining administrative tasks. However, the intersection of innovative AI technologies with sensitive patient data raises significant concerns about data security and patient privacy. Recent studies emphasize the importance of robust security measures and secure infrastructure to protect patient data while facilitating technological advancements (Ng et al., 2025; Pfeffer et al., 2024). These works outline the balance between fostering innovation and ensuring the confidentiality of sensitive health information, underscoring the need for a secure digital infrastructure. They also discuss the challenges healthcare institutions face in integrating LLMs, such as regulatory compliance, ethical considerations, and the need for centralized, compliant solutions. These insights are critical for stakeholders aiming to develop policies that support secure and ethical AI integration in healthcare.
Challenges and Developments
The adoption of LLMs in healthcare introduces several challenges, primarily centered around patient privacy and data security. A significant concern is the need to maintain the confidentiality of personal health information (PHI) while deploying AI systems that require vast amounts of data to function effectively. For example, LLMs could potentially analyze electronic health records (EHRs) to identify patterns and predict patient outcomes. However, this requires access to sensitive information, raising ethical and legal questions about data handling (Mittelstadt and Floridi, 2020).
Another challenge is regulatory compliance. Healthcare providers must navigate a complex landscape of regulations, such as HIPAA in the United States, which mandates strict standards for data protection. Additionally, ensuring that AI systems are transparent and explainable is crucial for maintaining trust among patients and healthcare professionals (Ng et al., 2025). Recent developments in secure multi-party computation and federated learning offer potential solutions by enabling AI models to learn from decentralized data sources without directly accessing them (Yang et al., 2023). These technologies could help mitigate privacy concerns while allowing LLMs to function effectively in healthcare environments.
Conclusion
Data governance plays a vital role in ensuring that LLMs can be harnessed in healthcare without compromising patient privacy. Effective data governance frameworks facilitate the secure and ethical use of AI by establishing clear guidelines for data access, usage, and sharing. These frameworks ensure that data is handled in compliance with relevant regulations, such as GDPR and HIPAA, thus protecting patient privacy. By implementing robust data governance strategies, healthcare organizations can create a secure environment for the deployment of LLMs, enabling them to harness the full potential of these technologies while maintaining the trust of patients and stakeholders (Ng et al., 2025; Pfeffer et al., 2024).
References:
Mittelstadt, B.D. and Floridi, L. (2020) ‘Ethical challenges posed by big data’, Perspectives in Biology and Medicine, 63(1), pp. 38-56. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC7819582/.
Ng, M.Y., Helzer, J., Pfeffer, M.A., Seto, T. and Hernandez-Boussard, T. (2025) ‘Development of secure infrastructure for advancing generative AI in healthcare’, Journal of the American Medical Informatics Association, 32(1), pp. 101-112. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC11833461/.
Pfeffer, M.A., Helzer, J., Ng, M.Y., Seto, T. and Hernandez-Boussard, T. (2024) ‘Development of secure infrastructure for advancing generative AI in healthcare’, Journal of the American Medical Informatics Association, 31(9), pp. 1645-1654. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC11469521/.
Yang, Q., Liu, Y., Chen, T. and Tong, Y. (2023) ‘Federated learning with privacy-preserving and model IP-right protection’, International Journal of Automation and Computing, 20(2), pp. 151-165. Available at: https://research.aber.ac.uk/files/63580947/s11633_022_1343_2.pdf.