Artificial intelligence-generated informed patient consent in various ophthalmological procedures: A comparative study of correctness, completeness, readability, and real-word application between Deepseek and Chatgpt 4o.

Abstract

Purpose: To study the correctness, completeness, language and readability, and real-world applicability of artificial intelligence chatbots-generated informed consent forms for various ophthalmological procedures and interventions.

Methods: A cross-sectional observational study was performed by ophthalmology faculties of a tertiary care eye hospital. A list of popularly performed ophthalmological interventions in ophthalmological operation theaters was compiled. Questions were created asking for informed consents. Each question was standardized; the age and diagnosis were mentioned, which were eventually fed into two publicly available chatbots, namely, ChatGPT 4o and Deepseek. The answers obtained from these chatbots were then evaluated on the basis of correctness, completeness, language and readability, additional relevant information, irrelevant information, and real-world applicability of the consent (word to word) in Indian Scenario. Chi-square tests were used for performing analysis of categorical data, namely, correctness and completeness, whereas Mann-Whitney U test was performed for numerical data.

Results: ChatGPT had less words and sentences compared to Deepseek; however, Deepseek offered a higher average readability score on both Flesch Kincaid calculator and Gunning Fog Index. Deepseek required more attempted to obtain the responses. However, 40% of the consents generated by both chatbots were not fit to be used in Indian scenarios.

Conclusion: Deepseek offered significantly more elaborate readable informed consents than ChatGPT; however, both the chatbots at present failed 40% of the times to create informed consents which can be used in Indian scenarios.