ABSTRACT
OBJECTIVES: This study assessed the ability of ChatGPT, an artificial intelligence(AI) language model, to determine the stage, grade, and extent of periodontitis based on the 2018 classification. MATERIALS AND METHODS: This study used baseline digital data of 200 untreated periodontitis patients to compare standardized reference diagnoses (RDs) with ChatGPT findings and determine the best criteria for assessing stage and grade. RDs were provided by four experts who examined each case. Standardized texts containing the relevant information for each situation were constructed to query ChatGPT. RDs were compared to ChatGPT's responses. Variables influencing the responses of ChatGPT were evaluated. RESULTS: ChatGPT successfully identified the periodontitis stage, grade, and extent in 59.5%, 50.5%, and 84.0% of cases, respectively. Cohen's kappa values for stage, grade and extent were respectively 0.447, 0.284, and 0.652. A multiple correspondence analysis showed high variance between ChatGPT's staging and the variables affecting the stage (64.08%) and low variance between ChatGPT's grading and the variables affecting the grade (42.71%). CONCLUSIONS: The present performance of ChatGPT in the classification of periodontitis exhibited a reasonable level. However, it is expected that additional improvements would increase its effectiveness and broaden its range of functionalities (NCT05926999). CLINICAL RELEVANCE: Despite ChatGPT's current limitations in accurately classifying periodontitis, it is important to note that the model has not been specifically trained for this task. However, it is expected that with additional improvements, the effectiveness and capabilities of ChatGPT might be enhanced.
Subject(s)
Artificial Intelligence , Periodontitis , Humans , Periodontitis/classification , Male , Female , Adult , Middle AgedABSTRACT
Objective: The objective of this study was to evaluate the effects of keratinized mucosa width (KMW) and mucosal thickness (MT) around dental implants on marginal bone loss (MBL). The evaluation was performed one year after loading by comparing clinical, radiographic, and biochemical parameters. Methods: The study included 87 implants in 87 patients undergoing regular follow-ups without hard or soft tissue augmentation one year after loading. Clinical measurements included plaque index (PI), gingival index (GI), bleeding on probing (BoP), probing depth (PD), KMW, and MT. MBL was assessed with periapical radiography. The peri-implant crevicular fluid (PICF) was analyzed for tumor necrosis factor-alpha (TNF-α), receptor activator of nuclear factor-kB ligand (RANKL), osteoprotegerin (OPG), and microRNA-27a. Results: The MBL of implants with thin MT (<2 mm) was higher than that of implants with thick MT (≥2 mm) (p < 0.05). A significant negative correlation (r: -0.217) was established between MT and MBL. No significant association was found between KMW and MBL (p > 0.05). No significant associations was found between KMW and MT with TNF-α, RANKL, OPG and RANKL/OPG (p > 0.05), with the exception of increased microRNA-27a levels in implants with KMW ≥ 2 mm (p < 0.05). Conclusions: Implants with a thick MT had a lower MBL. There may be an association between adequate KMW and high miRNA-27a levels. The relationship between MBL and miRNA-27a remains unclear.
ABSTRACT
Objectives The aim of this study is to evaluate the accuracy and completeness of the answers given by Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI OpCo, LLC, San Francisco, CA), to the most frequently asked questions on different topics in the field of periodontology. Methods The 10 most frequently asked questions by patients about seven different topics (periodontal diseases, peri-implant diseases, tooth sensitivity, gingival recessions, halitosis, dental implants, and periodontal surgery) in periodontology were created by ChatGPT. To obtain responses, a set of 70 questions was submitted to ChatGPT, with an allocation of 10 questions per subject. The responses that were documented were assessed using two distinct Likert scales by professionals specializing in the subject of periodontology. The accuracy of the responses was rated on a Likert scale ranging from one to six, while the completeness of the responses was rated on a scale ranging from one to three. Results The median accuracy score for all responses was six, while the completeness score was two. The mean scores for accuracy and completeness were 5.50 ± 0.23 and 2.34 ± 0.24, respectively. It was observed that ChatGPT's responses to the most frequently asked questions by patients for information purposes in periodontology were at least "nearly completely correct" in terms of accuracy and "adequate" in terms of completeness. There was a statistically significant difference between subjects in terms of accuracy and completeness (P<0.05). The highest and lowest accuracy scores were peri-implant diseases and gingival recession, respectively, while the highest and lowest completeness scores were gingival recession and dental implants, respectively. Conclusions The utilization of large language models has become increasingly prevalent, extending its applicability to patients within the healthcare domain. While ChatGPT may not offer absolute precision and comprehensive results without expert supervision, it is apparent that those within the field of periodontology can utilize it as an informational resource, albeit acknowledging the potential for inaccuracies.
