Francesco Fanelli, Muhammad Saleh, Pasquale Santamaria, Khrystyna Zhurakivska, Luigi Nibali, Giuseppe Troiano
{"title":"牙周病专业GPT - 40模型的开发和比较评价","authors":"Francesco Fanelli, Muhammad Saleh, Pasquale Santamaria, Khrystyna Zhurakivska, Luigi Nibali, Giuseppe Troiano","doi":"10.1111/jcpe.14101","DOIUrl":null,"url":null,"abstract":"BackgroundArtificial intelligence (AI) has the potential to enhance healthcare practices, including periodontology, by improving diagnostics, treatment planning and patient care. This study introduces ‘PerioGPT’, a specialized AI model designed to provide up‐to‐date periodontal knowledge using GPT‐4o and a novel retrieval‐augmented generation (RAG) system.MethodsPerioGPT was evaluated in two phases. First, its performance was compared against those of five other chatbots using 50 periodontal questions from specialists, followed by a validation with 71 questions from the 2023–2024 ‘In‐Service Examination’ of the American Academy of Periodontology (AAP). The second phase focused on assessing PerioGPT's generative capacity, specifically its ability to create complex and accurate periodontal questions.ResultsPerioGPT outperformed other chatbots, achieving a higher accuracy rate (81.16%) and generating more complex and precise questions with a mean complexity score of 3.81 ± 0.965 and an accuracy score of 4.35 ± 0.898. These results demonstrate PerioGPT's potential as a leading tool for creating reliable clinical queries in periodontology.ConclusionsThis study underscores the transformative potential of AI in periodontology, illustrating that specialized models can offer significant advantages over general language models for both educational and clinical applications. The findings highlight that tailoring AI technologies to specific medical fields may improve performance and relevance.","PeriodicalId":15380,"journal":{"name":"Journal of Clinical Periodontology","volume":"14 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Comparative Evaluation of a Reinstructed GPT‐4o Model Specialized in Periodontology\",\"authors\":\"Francesco Fanelli, Muhammad Saleh, Pasquale Santamaria, Khrystyna Zhurakivska, Luigi Nibali, Giuseppe Troiano\",\"doi\":\"10.1111/jcpe.14101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BackgroundArtificial intelligence (AI) has the potential to enhance healthcare practices, including periodontology, by improving diagnostics, treatment planning and patient care. This study introduces ‘PerioGPT’, a specialized AI model designed to provide up‐to‐date periodontal knowledge using GPT‐4o and a novel retrieval‐augmented generation (RAG) system.MethodsPerioGPT was evaluated in two phases. First, its performance was compared against those of five other chatbots using 50 periodontal questions from specialists, followed by a validation with 71 questions from the 2023–2024 ‘In‐Service Examination’ of the American Academy of Periodontology (AAP). The second phase focused on assessing PerioGPT's generative capacity, specifically its ability to create complex and accurate periodontal questions.ResultsPerioGPT outperformed other chatbots, achieving a higher accuracy rate (81.16%) and generating more complex and precise questions with a mean complexity score of 3.81 ± 0.965 and an accuracy score of 4.35 ± 0.898. These results demonstrate PerioGPT's potential as a leading tool for creating reliable clinical queries in periodontology.ConclusionsThis study underscores the transformative potential of AI in periodontology, illustrating that specialized models can offer significant advantages over general language models for both educational and clinical applications. The findings highlight that tailoring AI technologies to specific medical fields may improve performance and relevance.\",\"PeriodicalId\":15380,\"journal\":{\"name\":\"Journal of Clinical Periodontology\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Periodontology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/jcpe.14101\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Periodontology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jcpe.14101","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Development and Comparative Evaluation of a Reinstructed GPT‐4o Model Specialized in Periodontology
BackgroundArtificial intelligence (AI) has the potential to enhance healthcare practices, including periodontology, by improving diagnostics, treatment planning and patient care. This study introduces ‘PerioGPT’, a specialized AI model designed to provide up‐to‐date periodontal knowledge using GPT‐4o and a novel retrieval‐augmented generation (RAG) system.MethodsPerioGPT was evaluated in two phases. First, its performance was compared against those of five other chatbots using 50 periodontal questions from specialists, followed by a validation with 71 questions from the 2023–2024 ‘In‐Service Examination’ of the American Academy of Periodontology (AAP). The second phase focused on assessing PerioGPT's generative capacity, specifically its ability to create complex and accurate periodontal questions.ResultsPerioGPT outperformed other chatbots, achieving a higher accuracy rate (81.16%) and generating more complex and precise questions with a mean complexity score of 3.81 ± 0.965 and an accuracy score of 4.35 ± 0.898. These results demonstrate PerioGPT's potential as a leading tool for creating reliable clinical queries in periodontology.ConclusionsThis study underscores the transformative potential of AI in periodontology, illustrating that specialized models can offer significant advantages over general language models for both educational and clinical applications. The findings highlight that tailoring AI technologies to specific medical fields may improve performance and relevance.
期刊介绍:
Journal of Clinical Periodontology was founded by the British, Dutch, French, German, Scandinavian, and Swiss Societies of Periodontology.
The aim of the Journal of Clinical Periodontology is to provide the platform for exchange of scientific and clinical progress in the field of Periodontology and allied disciplines, and to do so at the highest possible level. The Journal also aims to facilitate the application of new scientific knowledge to the daily practice of the concerned disciplines and addresses both practicing clinicians and academics. The Journal is the official publication of the European Federation of Periodontology but wishes to retain its international scope.
The Journal publishes original contributions of high scientific merit in the fields of periodontology and implant dentistry. Its scope encompasses the physiology and pathology of the periodontium, the tissue integration of dental implants, the biology and the modulation of periodontal and alveolar bone healing and regeneration, diagnosis, epidemiology, prevention and therapy of periodontal disease, the clinical aspects of tooth replacement with dental implants, and the comprehensive rehabilitation of the periodontal patient. Review articles by experts on new developments in basic and applied periodontal science and associated dental disciplines, advances in periodontal or implant techniques and procedures, and case reports which illustrate important new information are also welcome.