{"title":"评估人工智能聊天机器人对发音障碍类型的诊断能力：模型开发与验证。","authors":"S Saeedi, M Aghajanzadeh","doi":"10.1016/j.anorl.2025.01.001","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>User-friendly artificial intelligence (AI) chatbots are increasingly being explored to assist healthcare teams in their decision-making processes. As accurate diagnosis in all medical fields is vital in treatment planning, this research seeks to explore the function of two specific AI chatbots, ChatGPT and Perplexity AI, in distinguishing the various types of dysphonia (organic, functional, and neurological).</p><p><strong>Material and methods: </strong>In experiment 1, a script combining voice self-assessments plus the acoustic analysis, and in experiment 2, only the acoustic analysis of 37 dysphonic patients was fed into the ChatGPT and Perplexity AI chatbots specifying the type and asked to develop a complex AI-based model to determine dysphonia type. Then, the same process was redone with data from a sample of 27 other patients as a test.</p><p><strong>Results: </strong>Although ChatGPT could not analyze the data and only provided guidance, the Cohen's Kappa agreement between experts' diagnoses and Perplexity AI diagnoses in experiment 1 (P=0.773) and experiment 2 (P=0.067) lacked statistically significance.</p><p><strong>Conclusion: </strong>Regarding the preliminary poor performance of AI chatbots in differential diagnosis of dysphonia type, it is not currently recommended to use them in clinical settings. However, modifications in AI chatbots in the future might provide more promising results in determining the dysphonia type. Further research is needed to shed light on AI chatbots ability in voice clinics.</p>","PeriodicalId":48834,"journal":{"name":"European Annals of Otorhinolaryngology-Head and Neck Diseases","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the diagnostic capacity of artificial intelligence chatbots for dysphonia types: Model development and validation.\",\"authors\":\"S Saeedi, M Aghajanzadeh\",\"doi\":\"10.1016/j.anorl.2025.01.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>User-friendly artificial intelligence (AI) chatbots are increasingly being explored to assist healthcare teams in their decision-making processes. As accurate diagnosis in all medical fields is vital in treatment planning, this research seeks to explore the function of two specific AI chatbots, ChatGPT and Perplexity AI, in distinguishing the various types of dysphonia (organic, functional, and neurological).</p><p><strong>Material and methods: </strong>In experiment 1, a script combining voice self-assessments plus the acoustic analysis, and in experiment 2, only the acoustic analysis of 37 dysphonic patients was fed into the ChatGPT and Perplexity AI chatbots specifying the type and asked to develop a complex AI-based model to determine dysphonia type. Then, the same process was redone with data from a sample of 27 other patients as a test.</p><p><strong>Results: </strong>Although ChatGPT could not analyze the data and only provided guidance, the Cohen's Kappa agreement between experts' diagnoses and Perplexity AI diagnoses in experiment 1 (P=0.773) and experiment 2 (P=0.067) lacked statistically significance.</p><p><strong>Conclusion: </strong>Regarding the preliminary poor performance of AI chatbots in differential diagnosis of dysphonia type, it is not currently recommended to use them in clinical settings. However, modifications in AI chatbots in the future might provide more promising results in determining the dysphonia type. Further research is needed to shed light on AI chatbots ability in voice clinics.</p>\",\"PeriodicalId\":48834,\"journal\":{\"name\":\"European Annals of Otorhinolaryngology-Head and Neck Diseases\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Annals of Otorhinolaryngology-Head and Neck Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.anorl.2025.01.001\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OTORHINOLARYNGOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Annals of Otorhinolaryngology-Head and Neck Diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.anorl.2025.01.001","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
Assessing the diagnostic capacity of artificial intelligence chatbots for dysphonia types: Model development and validation.
Purpose: User-friendly artificial intelligence (AI) chatbots are increasingly being explored to assist healthcare teams in their decision-making processes. As accurate diagnosis in all medical fields is vital in treatment planning, this research seeks to explore the function of two specific AI chatbots, ChatGPT and Perplexity AI, in distinguishing the various types of dysphonia (organic, functional, and neurological).
Material and methods: In experiment 1, a script combining voice self-assessments plus the acoustic analysis, and in experiment 2, only the acoustic analysis of 37 dysphonic patients was fed into the ChatGPT and Perplexity AI chatbots specifying the type and asked to develop a complex AI-based model to determine dysphonia type. Then, the same process was redone with data from a sample of 27 other patients as a test.
Results: Although ChatGPT could not analyze the data and only provided guidance, the Cohen's Kappa agreement between experts' diagnoses and Perplexity AI diagnoses in experiment 1 (P=0.773) and experiment 2 (P=0.067) lacked statistically significance.
Conclusion: Regarding the preliminary poor performance of AI chatbots in differential diagnosis of dysphonia type, it is not currently recommended to use them in clinical settings. However, modifications in AI chatbots in the future might provide more promising results in determining the dysphonia type. Further research is needed to shed light on AI chatbots ability in voice clinics.
期刊介绍：
European Annals of Oto-rhino-laryngology, Head and Neck diseases heir of one of the oldest otorhinolaryngology journals in Europe is the official organ of the French Society of Otorhinolaryngology (SFORL) and the the International Francophone Society of Otorhinolaryngology (SIFORL). Today six annual issues provide original peer reviewed clinical and research articles, epidemiological studies, new methodological clinical approaches and review articles giving most up-to-date insights in all areas of otology, laryngology rhinology, head and neck surgery. The European Annals also publish the SFORL guidelines and recommendations.The journal is a unique two-armed publication: the European Annals (ANORL) is an English language well referenced online journal (e-only) whereas the Annales Françaises d’ORL (AFORL), mail-order paper and online edition in French language are aimed at the French-speaking community. French language teams must submit their articles in French to the AFORL site.
Federating journal in its field, the European Annals has an Editorial board of experts with international reputation that allow to make an important contribution to communication on new research data and clinical practice by publishing high-quality articles.
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