Assessing the suitability of ChatGPT and Google Gemini for patient education on the surgical management of gynaecological malignancies
Artificial intelligence (AI)-based conversational tools like ChatGPT and Google Gemini are increasingly being explored for their potential in patient education. In gynaecological oncology, clear and accessible communication about surgical options such as hysterectomy is vital. This study investigates the effectiveness of these tools in generating patient-friendly educational content. Educational content on total hysterectomy, radical hysterectomy, laparoscopic hysterectomy, abdominal hysterectomy, and supracervical hysterectomy was generated using ChatGPT and Google Gemini. Responses were analysed for word count, sentence structure, grade level, ease score, similarity percentage, and reliability. An independent sample t test was used for comparison. Shapiro-Wilk and Levene’s tests were used to assess normality and variance equality, respectively. The analysis showed no statistically significant differences between ChatGPT and Google Gemini across all assessed metrics (p>0.05). Both tools produced similar content in terms of word count, sentence length, readability (ease score and grade level), and reliability. The average reliability score was slightly higher for Gemini, while ChatGPT showed slightly better readability. Both ChatGPT and Google Gemini demonstrate comparable effectiveness in generating suitable educational content on surgical management of gynaecological malignancies. With appropriate oversight, they can serve as supplementary tools in enhancing patient understanding and shared decision-making in clinical settings.
Authors
- Sharvari Kulkarni
- Sreya Nimmagadda
- Krishaa Nandini Suresh Babu
- Priyadarshini Jaishankar
Institutions
- Pilgrim Hospital (GB)
- Maharashtra Institute of Medical Education and Research (IN)
- Melmaruvathur Adhiparasakthi Institute of Medical Sciences and Research (IN)
Publication Details
- Journal
- International Journal of Reproduction Contraception Obstetrics and Gynecology
- Published
- 2026-10-29
- DOI
- https://doi.org/10.18203/2320-1770.ijrcog20264008
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00