Evaluating ChatGPT’s Effectiveness for Arabic Dry Mouth Patient Education - Abdullah Mohamed Alsoghier (Healthcare, 2026)
Background/Objectives: The present study aimed to assess the understandability and actionability of Arabic text generated by a large language model for commonly searched Arabic queries on dry mouth. Methods: Using Google Trends, the top 10 searches worldwide related to ‘oral dryness’ were entered in OpenAI’s Generative Pretrained Transformer 5.1. Generated texts were achieved independently. Assessments were performed using the Patient Education Materials Assessment Tool (PEMAT) to evaluate the content, word choice, and style. Results: Causes, symptoms, and treatment of dry mouth were the most common dry-mouth-related queries. The highly temporal distribution of search interests among Arabic-speaking countries peaked between 2020 and 2021, then remained high throughout 2023, before declining in November 2025. The mean PEMAT understandability and actionability scores were 89% and 80%, respectively. It was notable that all generated responses lacked visual aids, which could have made the content difficult to understand and insufficient for acting on the information. Moreover, the formal Arabic form of ‘causes of dry mouth’ with a glottal stop yielded lower actionability scores (60%) than the informal Arabic form (80%). Conclusions: Clinicians could actively supplement clinic-based discussions with advice on using large language models to help patients recognise dry mouth symptoms and improve self-care. Also, they could improve their effective adoption by clearly explaining expectations, limitations, and language/cultural differences when adopting these models.