The utility and accuracy of ChatGPT in providing post-operative instructions following tonsillectomy: A pilot study.

OBJECTIVE: To investigate the utility of answers generated by ChatGPT, a large language model, to common questions parents have for their children following tonsillectomy. METHODS: Twenty Otolaryngology residents anonymously submitted common questions asked by parents of pediatric patients following tonsillectomy. After identifying the 16 most common questions via consensus-based approach, we asked ChatGPT to generate responses to these queries. Satisfaction with the AI-generated answers was rated from 1 (Worst) to 5 (Best) by an expert panel of 3 pediatric Otolaryngologists. RESULTS: The distribution of questions across the five most common domains, their mean satisfaction scores, and their Krippendorf's interrater reliability coefficient were: Pain management [6, (3.67), (0.434)], Complications [4, (3.58), (-0.267)], Diet [3, (4.33), (-0.357)], Physical Activity [2, (4.33), (-0.318)], and Follow-up [1, (2.67), (-0.250)]. The panel noted that answers for diet, bleeding complications, and return to school were thorough. Pain management and follow-up recommendations were inaccurate, including a recommendation to prescribe codeine to children despite a black-box warning, and a suggested post-operative follow-up at 1 week, rather than the customary 2-4 weeks for our panel. CONCLUSION: Although ChatGPT can provide accurate answers for common patient questions following tonsillectomy, it sometimes provides eloquently written inaccurate information. This may lead to patients using AI-generated medical advice contrary to physician advice. The inaccuracy in pain management answers likely reflects regional practice variability. If trained appropriately, ChatGPT could be an excellent resource for Otolaryngologists and patients to answer questions in the postoperative period. Future research should investigate if Otolaryngologist-trained models can increase the accuracy of responses.

Features of Importance in Nasal Endoscopy: Deriving a Meaningful Framework.

OBJECTIVE: Critical components of the nasal endoscopic examination have not been definitively established for either the normal examination or for clinical disorders. This study aimed to identify concordance among rhinologists regarding the importance of examination findings for various nasal pathologies. STUDY DESIGN: A consortium of 19 expert rhinologists across the United States was asked to rank the importance of findings on nasal endoscopy for 5 different sinonasal symptom presentations. SETTING: An online questionnaire was distributed in July 2023. METHODS: The questionnaire utilized JotForm® software and featured 5 cases with a set of 4 identical questions per case, each covering a common indication for nasal endoscopy. Rankings were synthesized into Normalized Attention Scores (NASs) and Weighted Normalized Attention Scores (W-NASs) to represent the perceived importance of each feature, scaled from 0 to 1. RESULTS: General concordance was found for examination findings on nasal endoscopy within each case. The perceived features of importance differed between cases based on clinical presentation. For instance, in evaluating postnasal drip, the middle meatus was selected as the most important structure to examine (NAS, 0.73), with mucus selected as the most important abnormal finding (W-NAS, 0.66). The primary feature of interest for mucus was whether it was purulent or not (W-NAS, 0.67). Similar analyses were performed for features in each case. CONCLUSION: The implicit framework existing among rhinologists may help standardize examinations and improve diagnostic accuracy, augment the instruction of trainees, and inform the development of artificially intelligent algorithms to enhance clinical decision-making during nasal endoscopy.