The Future of Patient Education: AI-Driven Guide for Type 2 Diabetes

• Published in: Curēus (Palo Alto, CA) Vol. 15; no. 11; p. e48919
• Main Authors: Hernandez, Carlos A, Vazquez Gonzalez, Andres E, Polianovskaia, Anastasiia, Amoro Sanchez, Rafael, Muyolema Arce, Veronica, Mustafa, Ahmed, Vypritskaya, Ekaterina, Perez Gutierrez, Oscar, Bashir, Muhammad, Eighaei Sedeh, Ashkan
• Format: Journal Article
• Language: English
• Published: Palo Alto Cureus Inc 16-11-2023
• Subjects: Blood pressure; Chatbots; Diabetes; Gastrointestinal surgery; Glucose; Heart attacks; Insulin; Internal medicine; Kidneys; Patients; Physicians; Polyuria; Urination; Urine; Urology
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.48919

Introduction and aimThe surging incidence of type 2 diabetes has become a growing concern for the healthcare sector. This chronic ailment, characterized by its complex blend of genetic and lifestyle determinants, has witnessed a notable increase in recent times, exerting substantial pressure on healthcare resources. As more individuals turn to online platforms for health guidance and embrace the utilization of Chat Generative Pre-trained Transformer (ChatGPT; San Francisco, CA: OpenAI), a text-generating AI (TGAI), to get insights into their well-being, evaluating its effectiveness and reliability becomes crucial. This research primarily aimed to evaluate the correctness of TGAI responses to type 2 diabetes (T2DM) inquiries via ChatGPT. Furthermore, this study aimed to examine the consistency of TGAI in addressing common queries on T2DM complications for patient education.Material and methodsQuestions on T2DM were formulated by experienced physicians and screened by research personnel before querying ChatGPT. Each question was posed thrice, and the collected answers were summarized. Responses were then sorted into three distinct categories as follows: (a) appropriate, (b) inappropriate, and (c) unreliable by two seasoned physicians. In instances of differing opinions, a third physician was consulted to achieve consensus.ResultsFrom the initial set of 110 T2DM questions, 40 were dismissed by experts for relevance, resulting in a final count of 70. An overwhelming 98.5% of the AI's answers were judged as appropriate, thus underscoring its reliability over traditional online search engines. Nonetheless, a 1.5% rate of inappropriate responses underlines the importance of ongoing AI improvements and strict adherence to medical protocols.ConclusionTGAI provides medical information of high quality and reliability. This study underscores TGAI's impressive effectiveness in delivering reliable information about T2DM, with 98.5% of responses aligning with the standard of care. These results hold promise for integrating AI platforms as supplementary tools to enhance patient education and outcomes.