Evaluation of the Effectiveness, Safety, and Patient Satisfaction of Artificial Intelligence-Based Patient Education and Counseling for Both Recipients and Donors in the Preoperative and Postoperative Phases of Organ Transplantation.

Introduction: Effective patient education is critical in organ transplantation, particularly for living donor groups, where informed decision-making impacts both donors and recipients. AI-driven solutions, such as OpenAI's ChatGPT, can enhance education by providing real-time responses. This study assessed the effectiveness, safety, and satisfaction of an AI-supported patient education system integrated with WhatsApp and a Customer Relationship Management (CRM) system.

Methods: A prospective observational study was conducted at our transplant center between October 1, 2023, and July 31, 2024. Eligible participants included adults (18-65 years) who were either recipients or living donor candidates for kidney and liver transplantation. AI-generated responses were retrospectively evaluated by transplant physicians for accuracy and safety using a 5-point Likert scale. Patient satisfaction was assessed using the validated Turkish Short Assessment of Patient Satisfaction (SAPS) form.

Results: A total of 196 patients submitted 1281 questions, categorized into nine thematic groups. The most common inquiries pertained to post-transplant social life and work resumption (25.2%). AI responses demonstrated high safety (92% scoring ≥4) and accuracy (80% scoring ≥4). The highest accuracy was for surgical technique-related questions (4.7/5), while general questions had the lowest (4.2/5). Patient satisfaction was overwhelmingly positive, with 99.5% expressing satisfaction.

Conclusion: The AI-supported system provided accurate and safe preoperative and postoperative education for transplant patients, demonstrating high satisfaction. AI integration into clinical workflows presents a promising advancement, though challenges related to accuracy and ethics remain. Future research should explore AI's role in image recognition and triage to optimize transplant patient care.