Background: Total knee arthroplasty (TKA) is a surgical intervention that significantly improves patients’ quality of life, but the preoperative process can cause uncertainty, anxiety, and a lack of information. In recent years, artificial intelligence (AI)-powered chatbots and large language models have begun to play important roles in patient information processes in the healthcare field. In this study, the answers given by chat generative pretrained transformer (ChatGPT)-4.0 and DeepSeek-V3 AI programs to the 10 most frequent questions about TKA asked by patients before surgery were compared, and the effectiveness of AI in the patient information process was analyzed with the evaluations of orthopedists. Methods: Using Google Trends, patient forums, and clinical experiences, the 10 questions that TKA patients are most curious about in the preoperative, peroperative, and postoperative periods were determined. These questions were directed to ChatGPT-4.0 and DeepSeek-V3, and the answers were recorded. Five orthopedists (minimum 5 year surgical experienced) evaluated the answers using a Likert scale (1–5) according to criteria such as scientific accuracy, explanatory power, understandability for the patient, and detailed content. Results: The mean Likert score of ChatGPT-4.0 (4.7 ± 0.2) was found higher than the mean Likert score of DeepSeek-V3 (3.5 ± 0.3) (P < .05). ChatGPT-4.0 provided more comprehensive and detailed information, while DeepSeek-V3 provided superficial answers, especially in the answers to questions such as “life of the prosthesis,” “postoperative complications,” and “return to daily activities.” Conclusion: Our study showed that ChatGPT-4.0 is more effective than DeepSeek-V3 in terms of patient information regarding total knee replacement. It is emphasized that AI-supported systems are a fast and accessible source of information for patient education; however this information must be inspected by medical authorities for accuracy. Future studies should be conducted with larger patient populations, to increase the reliability of AI-based patient information systems and ensure their integration into clinical practice. Level of evidence: Level 5.

Background: Artificial intelligence has been increasingly used for radiographic fracture detection in recent years. However, its performance in the diagnosis of displaced and non-displaced fractures in specific anatomical regions has not been sufficiently investigated. This study aimed to evaluate the accuracy and sensitivity of Chat Generative Pretrained Transformer (ChatGPT-4.0) in the diagnosis of radial head, distal humerus and olecranon fractures. Methods: Anonymized radiographs, previously confirmed by an expert radiologist and orthopedist, were evaluated. Anteroposterior and lateral radiographs of 266 patients were analyzed. Each fracture site was divided into 2 groups: displaced and non-displaced. ChatGPT-4.0 asked 2 questions to indicate whether each image was broken. Responses were categorized as “fracture detected in the first question,” “fracture detected in the second question,” or “no fracture detected.” Results: ChatGPT-4.0 showed a significantly higher accuracy in diagnosing displaced fractures at all sites ( P < .001). The highest fracture detection rate in the first question was observed for displaced distal humeral fractures (87.7%). The success rate was significantly lower in non-displaced fractures, and in the non-displaced group the highest diagnostic rate was observed in radial head fractures (25.3%). No statistically significant difference was found in pairwise sensitivity comparisons between non-displaced fractures ( P > .05). Conclusion: ChatGPT-4.0 shows promising diagnostic performance in the detection of displaced olecranon, radial head and distal humeral fractures. However, its limited success in non-displaced fractures indicates that the model requires further training and development before clinical use. Level of evidence: Level 3.

Although several studies have demonstrated the successful use of wide-awake local anesthesia no tourniquet (WALANT) technique in cubital tunnel decompression, no prior study has investigated its use in anterior ulnar nerve transposition. This study aimed to retrospectively evaluate the outcomes of patients who underwent ulnar nerve decompression and subcutaneous anterior transposition using WALANT. A total of 41 surgeries were performed on 40 patients between August 2012 and March 2024 by a single surgeon. Patient data including age, sex, surgical side, Dellon satisfaction scale scores, postoperative complications, and satisfaction with anesthesia were collected. All procedures were performed using WALANT. Twelve (30%) patients were female and 28 (70%) were male and the mean postoperative follow-up period was 5.8 weeks (range 4–9 weeks). In the preoperative evaluation according to the Dellon scale, 12 (30%) patients had mild disease, 23 (57.5%) had moderate moderate, 5 (12.5%) had severe disease. Patient satisfaction was evaluated as bad in 3 patients (7.5%), moderate in 11 patients (27.5%) and good in 26 patients (65%). A significant negative correlation was observed between age and satisfaction questionnaire scores (r = −0.27, P = .023). Age also had a significant effect on the likelihood of conversion to general anesthesia (P = .0018). The WALANT anesthesia technique for anterior subcutaneous transposition of the ulnar nerve in cubital tunnel syndrome is safe and effective. The most important point in this regard is patient selection. A prospective cohort study with a larger cohort and longer follow-up time is required to monitor the long-term clinical outcomes.

Objective: The aim of the study is evaluating the realibity and quality and the reliability of Youtube video contents concerning calcaneal factures. Methods: We searched the terms of calcaneus fracture on Youtube and total of 57 videos evaluated. The following variables were collected for each video: number of views, duration in minutes and seconds, video source/uploader type, content type, days since upload, view ratio (views/day), and number of likes. The general features of the videos were evaluated in eight categories, video uploaders were evaluated in five categories, and video contents were evaluated in three categories. The Global Quality Scale (GQS), The Journal of the American Medical Association (JAMA) score, DISCERN scores were used to assess each video. Results: The median view ratio was 19.68 (interquartile range 9.39-37.3), median number of views were 28788 views. The median JAMA score was 2 (interquartile range (IQR) 2-3), GQS score was 3 (interquartile range 2-3) and DISCERN score was 39 (interquartile range 29,5-46). The most common video uploader were physicians (43.9%), disease specific information was the most viewed video content (52.6%). Academic sources were the best on median DISCERN (46), JAMA score (3) and GQS scores (3). Also disease specific disorders videos had the greatest median DISCERN (46), JAMA (3) and GQS scores (3). Conclusions: The Reliability, transparency and content of YouTube videos on calcaneus fractures measured by GQS and DISCERN scores are intermediate but JAMA scores were poor. Increasing video quality will provide better guidance to patients in such diseases like calcaneus fractures that are difficult to follow and treat.

Aim This study aimed to determine the clinicopathological spectrum and incidence of soft tissue tumors and tumor-like lesions of the fingers in Kocaeli and to evaluate whether occupational factors in a highly industrialized region influence their distribution. Methods Of 218 patients who underwent surgery for soft tissue masses of the hand and wrist between December 2014 and July 2025, 134 patients (61.4%) with masses located in the fingers were included. Patient demographics, lesion side (right/left), lesion location (dorsal/volar), clinical pre-diagnosis, and pathological diagnosis were analyzed. A p-value <0.05 was considered statistically significant. Results The mean age of patients was 47.6 ± 13.5 years, and 50.7% were male. Lesions were located on the right hand in 48.5% of cases, with the most frequently affected fingers being the third (32.8%) and first (25.4%) fingers. The most common tumor was the tendon sheath giant cell tumor (TSGCT) (37.3%), followed by the ganglion cyst (14.2%). Epidermal cysts were significantly more common in men (p = 0.039). The overall concordance between clinical and pathological diagnoses was 76.1%, with fibroma being the most frequently misdiagnosed lesion. Conclusions All excised soft tissue tumors and tumor-like lesions of the fingers were benign. TSGCT was the most common tumor in this series, with a higher prevalence than previously reported in the literature. Clinical diagnoses are generally reliable, although rare lesions can be challenging. Because complete excision is particularly important for TSGCT and glomus tumors, combining clinical, radiological, and histopathological evaluation of finger masses can help ensure accurate diagnosis and appropriate treatment.