Balancing Innovation and Privacy: Addressing Surveillance Concerns in Healthcare AI Systems

Abstract

Healthcare AI systems promise revolutionary advancements in opinion, treatment, and care delivery, yet produce unknown sequestration challenges as these technologies bear vast patient datasets to serve effectively. This pressure between invention and sequestration protection represents an abecedarian incongruity in healthcare's digital metamorphosis. Recent sequestration breaches, instigated by controversial data-participating hookups between healthcare systems and technology companies, have eroded public trust and stressed crunches in traditional concurrence models and nonsupervisory fabrics. The composition examines both specialized results, including allied literacy, discriminational sequestration, homomorphic encryption, and synthetic data generation, alongside governance fabrics emphasizing transparent concurrence mechanisms, specialized institutional review processes, multistakeholder involvement, and streamlined regulations. Addressing these challenges requires a balanced approach that preserves sequestration without stifling salutary invention, eventually maintaining the patient trust essential for healthcare advancement.