Improving Patient Data Privacy and Authentication Protocols against AI-Powered Phishing Attacks in Telemedicine
Olufisayo Juliana Tiwo
*
University of Lagos, University Road Lagos Mainland Akoka, Yaba, Lagos, Nigeria.
Temilade Oluwatoyin Adesokan-Imran
University of Ibadan, Oduduwa Road, 200132, Ibadan, Oyo, Nigeria.
Damilola Comfort Babarinde
Kyiv Medical University Ukraine 2, Boryspilska Street, Kyiv-02099, Ukraine.
Isaac Adinoyi Salami
University of Tampa, 12911 Firth CT. 33612, Tampa FL, United States.
Ogechukwu Scholastica Onyenaucheya
Prairie View A& M University, Texas, 100 University Drive, Prairie View, Texas 77446, United States.
Oluwaseun Oladeji Olaniyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
*Author to whom correspondence should be addressed.
Abstract
Telemedicine’s rapid expansion has improved healthcare accessibility but has also increased cybersecurity risks, particularly AI-powered phishing attacks that exploit authentication vulnerabilities. Patient data breaches are rising due to sophisticated phishing schemes targeting healthcare providers and patients. This study analyzes the impact of AI-driven phishing breaches using data from the HHS Breach Reports, Verizon DBIR, IBM Cost of a Data Breach Report, and PhishTank Open Phishing Dataset. Employing trend analysis, logistic regression, ANOVA, and machine learning classification, the findings reveal a 60% increase in patient record exposure due to AI-powered phishing since 2021, with credential theft contributing most to authentication failures (coefficient = 1.75). The study also finds that blockchain authentication reduces financial losses to $4.5M per breach, significantly lower than the $12M incurred by unprotected organizations. AI-based phishing detection achieves a recall rate of 90.5% but suffers from a 47.6% false-negative rate, indicating the need for refinement. Recommendations include implementing adaptive AI-driven threat detection, behavioral biometrics, blockchain authentication, and stronger regulatory oversight.
Keywords: AI-powered phishing, telemedicine cybersecurity, authentication vulnerabilities, blockchain authentication, phishing detection