Assessing AI-Generated Autism Information for Healthcare Use: A Cross-Linguistic and Cross-Geographic Evaluation of ChatGPT, Gemini, and Copilot

Abstract

Background/Objectives: Autism is one of the most prevalent neurodevelopmental conditions globally, and healthcare professionals including pediatricians, developmental specialists, and speech-language pathologists, play a central role in guiding families through diagnosis, treatment, and support. As caregivers increasingly turn to digital platforms for autism-related information, artificial intelligence (AI) tools such as ChatGPT, Gemini, and Microsoft Copilot are emerging as popular sources of guidance. However, little is known about the quality, readability, and reliability of information these tools provide. This study conducted a detailed comparative analysis of three widely used AI models within defined linguistic and geographic contexts to examine the quality of autism-related information they generate. Methods: Responses to 44 caregiver-focused questions spanning two key domains-foundational knowledge and practical supports-were evaluated across three countries (USA, England, and T & uuml;rkiye) and two languages (English and Turkish). Responses were coded for accuracy, readability, actionability, language framing, and reference quality. Results: Results showed that ChatGPT generated the most accurate content but lacked reference transparency; Gemini produced the most actionable and well-referenced responses, particularly in Turkish; and Copilot used more accessible language but demonstrated lower overall accuracy. Across tools, responses often used medicalized language and exceeded recommended readability levels for health communication. Conclusions: These findings have critical implications for healthcare providers, who are increasingly tasked with helping families evaluate and navigate AI-generated information. This study offers practical recommendations for how providers can leverage the strengths and mitigate the limitations of AI tools when supporting families in autism care, especially across linguistic and cultural contexts.