aceQuality, Readability, and Reliability of Artificial Intelligence Chatbot Responses to Parent-Centered Questions on Newborn Hearing Screening: A Cross-Sectional Comparative Study
Aims and objectives: Parents increasingly use large language models (LLMs) to obtain health-related information; however, the quality, readability, and reliability of AI-generated patient education remain uncertain. This study evaluated the performance of three LLM-based chatbots in providing parent-centred information regarding newborn hearing screening. Methods: Thirty-eight parent-centred questions regarding newborn hearing screening were submitted to ChatGPT-5.1, Gemini 2.5 Flash, and DeepSeek-V3. Responses…
Three chatbots answered 38 parent questions on newborn hearing screening at acceptable quality, with mEQIP scores indicating good quality and DeepSeek-V3 leading on accuracy and comprehensiveness.
Chatbot answers on newborn hearing screening stayed relatively hard for parents to read, with Flesch Reading Ease scores of 51.5-59.3 indicating difficult readability and quality varying across models.
The authors say substantial variability was observed across models and that further research is needed before routine clinical use.
Evidence
- Peer-reviewedJournal of Evaluation in Clinical Practice2026-12-01
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Truvace Impact Record TRV-2026-2374, v1: “Quality, Readability, and Reliability of Artificial Intelligence Chatbot Responses to Parent-Centered Questions on Newborn Hearing Screening: A Cross-Sectional Comparative Study.” Truvace, 2026-11-05. /record/TRV-2026-2374 (accessed at citation time). sha256 9dd4058e3631d374…
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