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.

Background: Patients with hallux valgus increasingly seek health information through consumer-facing artificial intelligence (AI)–driven patient education tools, particularly large language model–based conversational agents. Although these tools offer rapid and accessible responses, concerns remain regarding the reliability, usefulness, overall quality, and readability of AI-generated patient education materials. Evidence specifically evaluating AI-generated patient education for hallux valgus, a condition strongly influenced by patient expectations and treatment preferences, remains limited. Methods: This cross-sectional comparative study evaluated the performance of three large language models—ChatGPT-4o, Gemini-2.5-Flash, and DeepSeek-V3—in responding to 20 patient-centered questions related to hallux valgus. Questions were developed using AI-assisted question generation and publicly available Google Trends search patterns and categorized into four clinical domains. AI-generated responses were anonymized and independently assessed by three orthopaedic surgeons for reliability, usefulness, and overall quality using 7-point Likert-based reliability and usefulness scales and the Global Quality Scale (GQS). Readability was analyzed using six standardized indices. Inter-rater agreement and between-model comparisons were statistically evaluated. Results: Gemini-2.5-Flash demonstrated modestly higher overall reliability, particularly in questions related to etiology and clinical presentation. DeepSeek-V3 achieved higher usefulness scores in the long-term outcomes and quality-of-life domain and produced significantly more readable content, as reflected by higher Flesch Reading Ease scores and lower grade-level indices. In contrast, Gemini-2.5-Flash generated linguistically more complex responses requiring higher educational levels for comprehension. Overall usefulness and global quality scores did not differ significantly among models. Qualitative review also identified occasional examples of oversimplified or potentially misleading information. Despite these differences, several readability metrics exceeded recommended patient health-literacy thresholds. Conclusions: Contemporary AI-based conversational agents can provide patient-oriented information regarding hallux valgus with variable reliability and readability characteristics, although statistically significant differences were observed across models. A trade-off between factual accuracy and linguistic accessibility was observed. AI tools should therefore be regarded as adjuncts to, rather than replacements for, clinician-led patient education. Awareness of AI limitations and appropriate clinical guidance remain essential to ensure safe, accurate, and patient-centered information delivery. Clinical trial number: Not applicable.

Purpose: The aim of the study was to determine the effect of trauma etiology on the management, functional outcomes and psychiatric characteristics of suicide jumpers and patients who accidentally fall from height. Methods: 89 patients (48 accidental falls and 41 suicidal jumpers) who had undergone spinal surgery were included in the study. The patients were evaluated clinically and radiologically. Visual Analog Scale (VAS), Roland Morris Disability Questionnaire (RMDQ) and McGill Pain Questionnaire (MPQ) were performed for functional evaluation. BECK hopelessness scale (BHS), BECK depression inventory (BDI), and SF-36 scales were used for the psychiatric evaluation. All outcomes were compared between suicidal jumpers and accidental falls. Results: The RMDQ and ODI questionnaires stated a higher disability in the suicide jump group (p = 0.001 and p = 0.029, respectively). However, the VAS and MPQ questionnaires did not differ in significance between groups (p = 0.182 and p = 0.306, respectively). The SF-36 scale showed that physical function, role emotional, vitality and mental health subdomains were worse in the suicide jump group (p = 0.001, p = 0.029, p = 0.014 and p = 0.030, respectively). BDI scores were significantly higher in the suicide jump group while no difference was observed between the groups in terms of BSH (p = 0.017 and p = 0.940, respectively). Conclusion: Psychiatric disorders are more common in patients in the suicidal jumpers. The presence of underlying psychiatric problems adversely affects the postoperative functional outcomes of patients with surgically treated spinal fractures. A multidisciplinary approach together with raising awareness in this way can improve the clinical outcomes after orthopedic treatment, even if there is physical disability.