Search for medical information for chronic rhinosinusitis through an artificial intelligence ChatBot

Arsany Yassa; Olivia Ayad; David Avery Cohen; Aman M Patel; Ved A Vengsarkar; Michael S Hegazin; Andrey Filimonov; Wayne D Hsueh; Jean Anderson Eloy

doi:10.1002/lio2.70009

Search for medical information for chronic rhinosinusitis through an artificial intelligence ChatBot

Laryngoscope Investig Otolaryngol. 2024 Sep 9;9(5):e70009. doi: 10.1002/lio2.70009. eCollection 2024 Oct.

Authors

Arsany Yassa¹, Olivia Ayad^{2

3

4}, David Avery Cohen⁵, Aman M Patel¹, Ved A Vengsarkar¹, Michael S Hegazin¹, Andrey Filimonov¹, Wayne D Hsueh^{1

6

7}, Jean Anderson Eloy^{1

6

7

8

9}

Affiliations

¹ Department of Otolaryngology-Head and Neck Surgery Rutgers New Jersey Medical School Newark New Jersey USA.
² Department of Architecture and Territory Mediterranean University of Reggio Calabria Calabria Italy.
³ Department of Landscape Architecture International Credit Hours Engineering Programs of Ain Shams University Cairo Egypt.
⁴ Arclivia Bayonne NJ United States.
⁵ Department of Otolaryngology-Head and Neck Surgery University of Florida College of Medicine Gainesville Florida USA.
⁶ Department of Ophthalmology and Visual Science Rutgers New Jersey Medical School Newark New Jersey USA.
⁷ Center for Skull Base and Pituitary Surgery, Neurological Institute of New Jersey Rutgers New Jersey Medical School Newark New Jersey USA.
⁸ Department of Neurological Surgery Rutgers New Jersey Medical School Newark New Jersey USA.
⁹ Department of Otolaryngology and Facial Plastic Surgery Cooperman Barnabas Medical Center-RWJBarnabas Health Livingston New Jersey USA.

Abstract

Objectives: Artificial intelligence is evolving and significantly impacting health care, promising to transform access to medical information. With the rise of medical misinformation and frequent internet searches for health-related advice, there is a growing demand for reliable patient information. This study assesses the effectiveness of ChatGPT in providing information and treatment options for chronic rhinosinusitis (CRS).

Methods: Six inputs were entered into ChatGPT regarding the definition, prevalence, causes, symptoms, treatment options, and postoperative complications of CRS. International Consensus Statement on Allergy and Rhinology guidelines for Rhinosinusitis was the gold standard for evaluating the answers. The inputs were categorized into three categories and Flesch-Kincaid readability, ANOVA and trend analysis tests were used to assess them.

Results: Although some discrepancies were found regarding CRS, ChatGPT's answers were largely in line with existing literature. Mean Flesch Reading Ease, Flesch-Kincaid Grade Level and passive voice percentage were (40.7%, 12.15%, 22.5%) for basic information and prevalence category, (47.5%, 11.2%, 11.1%) for causes and symptoms category, (33.05%, 13.05%, 22.25%) for treatment and complications, and (40.42%, 12.13%, 18.62%) across all categories. ANOVA indicated no statistically significant differences in readability across the categories (p-values: Flesch Reading Ease = 0.385, Flesch-Kincaid Grade Level = 0.555, Passive Sentences = 0.601). Trend analysis revealed readability varied slightly, with a general increase in complexity.

Conclusion: ChatGPT is a developing tool potentially useful for patients and medical professionals to access medical information. However, caution is advised as its answers may not be fully accurate compared to clinical guidelines or suitable for patients with varying educational backgrounds.Level of evidence: 4.

Keywords: ICAR; artificial intelligence; chronic rhinosinusitis; medical information systems; readability.