The integration of artificial intelligence in healthcare has improved diagnostic accuracy, treatment, and operational efficiency, it also raises concerns regarding patient data privacy and security. This study investigates the intersection between AI implementation in healthcare and the protection of sensitive patient information. This research identifies key data privacy challenges inherent to these technologies. The study explores issues such as data collection and consent mechanisms, algorithmic transparency and re-identification risks in anonymized datasets, unauthorized access vulnerabilities. Data were analyzed using appropriate parametric and non-parametric statistical tests including t-tests, ANOVA and chi-square analyses to examine the relationships between AI implementation and data privacy outcomes. The study measured privacy impact through multiple dimensions including privacy breach incidents, compliance with regulatory standards, security protocol adherence, and self-reported awareness levels among different categories of healthcare professionals.
Saraf et al. (Fri,) studied this question.