Work

MicrotiaGPT : Appraisal of Microtia Care Recommendations From an Artificially Intelligent Chatbot

Year: 2026

Type: article

Abstract: ABSTRACT Objective Evaluate the accuracy, comprehensiveness, and similarity to provider response of ChatGPT‐4o in providing patient education regarding microtia and aural atresia management. Methods Ten standardized inquiries were created across three domains (General Information, Hearing/Anatomy, Treatment Decision‐Making) and entered into ChatGPT‐4o. Responses were evaluated by an international multidisciplinary cohort of 23 experts, including pediatric otolaryngologists and audiologists. Using 5‐point Likert scales, graders assessed factual accuracy, comprehensiveness, similarity to physician responses, and overall quality. Readability was analyzed using Flesch–Kincaid metrics. Results ChatGPT‐generated responses demonstrated high accuracy (mean = 4.03 [SD = 0.66]), comprehensiveness (mean = 4.18 [SD = 0.54]), and similarity to the provider's response (4.02 [0.70]). Audiologists assigned significantly lower ratings than pediatric otolaryngologists regarding accuracy (mean = 3.76 vs. 4.13; p < 0.001) and comprehensiveness (3.94 vs. 4.30; p = 0.046). Qualitative analysis included concerns regarding advanced terminology, omission of specific risks, and a lack of empathetic framing regarding parental guilt. Flesch–Kincaid Reading Grade Level often exceeded the 5th‐grade average level for patient education materials (mean = 9.64 [SD = 1.21]). Conclusion ChatGPT‐4o provides reliable introductory information but lacks the technical nuance and multidisciplinary coordination essential for complex microtia care. While a useful adjunct for early information‐seeking, ChatGPT‐4o lacks the multidisciplinary framing and depth required for ideal counseling.

Source: Laryngoscope Investigative Otolaryngology

DOI: 10.1002/lio2.70526

Authors: Oluwatobiloba Ayo‐Ajibola, Simon Park, Beth Osterbauer, Krystina Kallemeyn, Gabriel Gomez

Institutions: University of Southern California, Children's Hospital of Los Angeles, Baylor College of Medicine, Texas Children's Hospital

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Quality education

Open Access status: gold

Estimated APC paid: $2,900