PAINe: An Artificial Intelligence-based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin.

Autor:	de Araujo BMM; School of Dentistry, Department of Endodontics, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil., de Jesus Freitas PF; School of Dentistry, Department of Endodontics, Tuiuti University of Paraná, Curitiba, Paraná, Brazil., Deliga Schroder AG; School of Dentistry, Department of Endodontics, Tuiuti University of Paraná, Curitiba, Paraná, Brazil., Küchler EC; Department of Orthodontics, University Hospital Bonn, Medical Faculty, Bonn, Germany., Baratto-Filho F; School of Dentistry, Department of Endodontics, Tuiuti University of Paraná, Curitiba, Paraná, Brazil; University of the Region of Joinville (Univille), Joinville, Santa Catarina, Brazil., Ditzel Westphalen VP; School of Dentistry, Department of Endodontics, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil., Carneiro E; School of Dentistry, Department of Endodontics, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil., Xavier da Silva-Neto U; School of Dentistry, Department of Endodontics, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil., de Araujo CM; School of Dentistry, Department of Endodontics, Tuiuti University of Paraná, Curitiba, Paraná, Brazil. Electronic address: cristiano.araujo@utp.br.
Jazyk:	angličtina
Zdroj:	Journal of endodontics [J Endod] 2024 Sep 27. Date of Electronic Publication: 2024 Sep 27.
DOI:	10.1016/j.joen.2024.09.008
Abstrakt:	Introduction: Pain associated with temporomandibular dysfunction (TMD) is often confused with odontogenic pain, which is a challenge in endodontic diagnosis. Validated screening questionnaires can aid in the identification and differentiation of the source of pain. Therefore, this study aimed to develop a virtual assistant based on artificial intelligence using natural language processing techniques to automate the initial screening of patients with tooth pain. Methods: The PAINe chatbot was developed in Python (Python Software Foundation, Beaverton, OR) language using the PyCharm (JetBrains, Prague, Czech Republic) environment and the openai library to integrate the ChatGPT 4 API (OpenAI, San Francisco, CA) and the Streamlit library (Snowflake Inc, San Francisco, CA) for interface construction. The validated TMD Pain Screener questionnaire and 1 question regarding the current pain intensity were integrated into the chatbot to perform the differential diagnosis of TMD in patients with tooth pain. The accuracy of the responses was evaluated in 50 random scenarios to compare the chatbot with the validated questionnaire. The kappa coefficient was calculated to assess the agreement level between the chatbot responses and the validated questionnaire. Results: The chatbot achieved an accuracy rate of 86% and a substantial level of agreement (κ = 0.70). Most responses were clear and provided adequate information about the diagnosis. Conclusions: The implementation of a virtual assistant using natural language processing based on large language models for initial differential diagnosis screening of patients with tooth pain demonstrated substantial agreement between validated questionnaires and the chatbot. This approach emerges as a practical and efficient option for screening these patients. (Copyright © 2024 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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