THE EVOLUTION OF AI MEDICAL CONSULTANTS AND THEIR IMPACT ON PATIENT EDUCATION: A LITERATURE REVIEW - Olejnik, Agnieszka; Możdżan, Maria; Biskup, Laura; Zheng, Edward; Huszcza, Beata; Gronek, Konrad; Kaczmarek, Julia; Grandos, Jakub; Gajewski, Dominik; Głowacka, Zuzanna; Kościołek, Kinga (International Journal of Innovative Technologies in Social Science, 2026)
Background: The rapid development of Artificial Intelligence (AI) has led to its transition from a theoretical concept in informatics to a functional clinical tool. This study evaluates the impact of AI-based consultants and Large Language Models (LLMs) on patient health literacy and the quality of medical education within the framework of contemporary healthcare standards. Methods: A systematic literature review was conducted in accordance with PRISMA guidelines. Databases including PubMed, Embase, and the Cochrane Library were searched for peer-reviewed publications from 2021 to 2025. Following a rigorous selection process based on Evidence-Based Medicine (EBM) criteria, 31 key sources were identified and synthesized. Results: LLMs significantly improve the comprehensibility of clinical terminology by adapting medical content to a 9th-11th-grade literacy level. Digital feedback systems, such as the KidneyOnline platform, demonstrate superior efficacy in improving patient adherence compared with standard protocols. However, several critical limitations were identified, including hallucination bias (fabrication of clinical data), a “readability floor” phenomenon associated with up to 83% content reduction, and the risk of professional deskilling among clinicians. Furthermore, wearable devices such as the Apple Watch show high correlation (r=0.92) with medical-grade pulse oximetry, offering new perspectives for real-time health monitoring. Conclusions: AI represents a transformative yet socially consequential innovation in patient education and health literacy. Its responsible implementation requires a strict human-in-the-loop model to ensure clinical safety, ethical accountability, and equitable access. Systematic verification of AI-generated medical information by qualified healthcare professionals remains essential to maintain both high standards of care and public trust in digital health innovations.