Background/Objectives: Rotator cuff (RC) tears are a leading cause of shoulder pain and disability. Artificial intelligence (AI)-based chatbots are increasingly applied in healthcare for diagnostic support and patient education, but the reliability, quality, and readability of their outputs remain uncertain. International guidelines (AMA, NIH, European health communication frameworks) recommend that patient materials be written at a 6th–8th grade reading level, yet most online and AI-generated content exceeds this threshold. Methods: We compared responses from three AI chatbots—ChatGPT-4o (OpenAI), Gemini 1.5 Flash (Google), and DeepSeek-V3 (Deepseek AI)—to 20 frequently asked patient questions about RC tears. Four orthopedic surgeons independently rated reliability and usefulness (7-point Likert) and overall quality (5-point Global Quality Scale). Readability was assessed using six validated indices. Statistical analysis included Kruskal–Wallis and ANOVA with Bonferroni correction; inter-rater agreement was measured using intraclass correlation coefficients (ICCs). Results: Inter-rater reliability was good to excellent (ICC 0.726–0.900). Gemini 1.5 Flash achieved the highest reliability and quality, ChatGPT-4o performed comparably but slightly lower in diagnostic content, and DeepSeek-V3 consistently scored lowest in reliability and quality but produced the most readable text (FKGL ≈ 6.5, within the 6th–8th grade target). None of the models reached a Flesch Reading Ease (FRE) score above 60, indicating that even the most readable outputs remained more complex than plain-language standards. Conclusions: Gemini 1.5 Flash and ChatGPT-4o generated more accurate and higher-quality responses, whereas DeepSeek-V3 provided more accessible content. No single model fully balanced accuracy and readability. Clinical Implications: Hybrid use of AI platforms—leveraging high-accuracy models alongside more readable outputs, with clinician oversight—may optimize patient education by ensuring both accuracy and accessibility. Future work should assess real-world comprehension and address the legal, ethical, and generalizability challenges of AI-driven patient education.

Purpose: The aim of this study was to comparatively evaluate the responses generated by three advanced artificial intelligence (AI) models, ChatGPT‐4o (OpenAI), Gemini 1.5 Flash (Google) and DeepSeek‐V3, to frequently asked patient questions about meniscal tears in terms of reliability, usefulness, quality, and readability. Methods: Responses from three AI chatbots, ChatGPT‐4o (OpenAI), Gemini 1.5 Flash (Google) and DeepSeek‐V3 (DeepSeek AI), were evaluated for 20 common patient questions regarding meniscal tears. Three orthopaedic specialists independently scored reliability and usefulness on 7‐point Likert scales and overall response quality using the 5‐point Global Quality Scale. Readability was analysed with six established indices. Inter‐rater agreement was examined with intraclass correlation coefficients (ICCs) and Fleiss’ Kappa, while between‐model differences were tested using Kruskal–Wallis and ANOVA with Bonferroni adjustment. Results: Gemini 1.5 Flash achieved the highest reliability, significantly outperforming both GPT‐4o and DeepSeek‐V3 ( p = 0.001). While usefulness scores were broadly similar, Gemini was superior to DeepSeek‐V3 ( p = 0.045). Global Quality Scale scores did not differ significantly among models. In contrast, GPT‐4o consistently provided the most readable content ( p < 0.001). Inter‐rater reliability was excellent across all evaluation domains (ICC > 0.9). Conclusion: All three AI models generated high‐quality educational content regarding meniscal tears. Gemini 1.5 Flash demonstrated the highest reliability and usefulness, while GPT‐4o provided significantly more readable responses. These findings highlight the trade‐off between reliability and readability in AI‐generated patient education materials and emphasise the importance of physician oversight to ensure safe, evidence‐based integration of these tools into clinical practice. Level of Evidence: Level V, observation‐based, expert opinion‐based, or in vitro/artificial intelligence model evaluation.

Determining the optimal amputation level in diabetic foot (DF) and peripheral artery disease (PAD) patients is crucial for minimizing reamputation risk while preserving functionality. The Society for Vascular Surgery Wound, Ischemia, and Foot Infection (SVS WIfI) and International Working Group on Diabetic Foot (IWGDF) scoring systems are widely used for risk stratification, but their predictive value for reamputation remains unclear. The current study aimed to evaluate the predictive utility of these scoring systems for reamputation risk in patients undergoing amputations due to DF or PAD. A retrospective cohort study was conducted on 108 patients who underwent unilateral amputations due to DF or PAD between 2016 and 2021. Patients were categorized based on WIfI and IWGDF classifications, and clinical outcomes, including reamputation rates, ICU admissions, and mortality, were analyzed using logistic regression and post hoc analyses. Reamputation occurred in 41 (38%) patients. Higher WIfI (OR: 3.85, 95% CI: 2.10-7.05, P = .001) and severe IWGDF scores (OR: 3.25, 95% CI: 1.80-5.88, P = .008) significantly correlated with increased reamputation risk. Patients with high-risk scores also exhibited higher ICU admission ( P = .03) and mortality rates ( P = .01). Male gender (OR: 3.47, 95% CI: 1.45-8.31, P = .005), diabetes (OR: 2.32, 95% CI: 1.10-4.90, P = .027), and prolonged hospitalization (OR: 1.04, 95% CI: 1.01-1.08, P = .021) were independent predictors, while above-ankle amputations were protective for reamputation (OR: 0.22, 95% CI: 0.11-0.43, P < .001). Both scoring systems are effective in predicting reamputation risk, with WIfI providing a more comprehensive risk assessment. Combining these tools may enhance decision-making and improve patient outcomes in terms of reamputation prevention, hospital stay, ICU admission, and mortality.

Aims: Artificial Intelligence (AI)–based language models are increasingly used to generate medical information and patient education materials. However, the reliability and safety of AI-generated rehabilitation guidance remain uncertain. This study aimed to evaluate the accuracy, safety, clinical utility, and readability of rehabilitation recommendations generated by ChatGPT-5 for Bankart lesions and to compare these outputs with expert-developed rehabilitation protocols.Methods: A blinded, cross-sectional comparative quality assessment was conducted. Standardized prompts regarding nonoperative and postoperative Bankart rehabilitation were used to generate responses from ChatGPT-5. AI-generated texts were compared with protocols prepared by a panel of orthopedic shoulder surgeons and an experienced physiotherapist. All texts were anonymized and independently evaluated by three blinded expert raters using a structured 5-point Likert scale assessing clinical accuracy, safety, actionability, comprehensiveness, and overall quality. Major clinical errors were recorded separately. Readability was assessed using Flesch Reading Ease and Flesch–Kincaid Grade Level scores. Inter-rater reliability was analyzed using intraclass correlation coefficients (ICC). Results: A total of 20 rehabilitation texts (10 AI-generated and 10 expert-developed) were evaluated. Expert protocols demonstrated significantly higher scores in clinical accuracy (4.6±0.4 vs 3.4±0.7, p