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Background: Robotic surgical systems offer enhanced precision, motion scaling, tremor filtration, and visualization, making them highly suitable for the complex anatomical demands of plastic and reconstructive surgery. While widely implemented in other specialties, their integration in plastic surgery remains limited. This systematic review evaluates the clinical applications, outcomes, and limitations of robotic-assisted techniques in plastic and reconstructive procedures. Methods: Following PRISMA guidelines, a systematic search was conducted across PubMed, Embase, Scopus, and Web of Science for studies published between January 1980 and March 2025. Clinical studies reporting robotic applications in plastic surgery were included, while cadaveric, animal, and non-English studies were excluded. Data extraction and quality assessment were performed using Covidence and validated tools including the CARE checklist, NOS, GRADE, and SYRCLE. A total of 1428 studies were screened, and 31 met the inclusion criteria. Results: Robotic systems were primarily applied in microsurgery (n = 16), breast reconstruction (n = 8), and craniofacial/aesthetic surgery (n = 7). Common platforms included the Symani Surgical System, Da Vinci systems, and ARTAS. Robotic-assisted approaches improved precision, aesthetic outcomes, flap survival, and patient satisfaction, particularly in procedures involving lymphaticovenous anastomosis and nipple-sparing mastectomy. However, challenges included steep learning curves, longer operative times, high equipment costs, and the lack of haptic feedback. Quality assessment rated all studies as moderate. Conclusions: Robotic-assisted surgery demonstrates considerable potential in enhancing plastic and reconstructive outcomes. As systems become more compact, cost-effective, and integrated with AI and biomimetic technologies, their broader adoption is anticipated. Further high-quality studies are needed to optimize these systems and support widespread clinical implementation.

Artificial intelligence (AI) chatbots, such as ChatGPT, have made significant progress. These chatbots, particularly popular among health care professionals and patients, are transforming patient education and disease experience with personalized information. Accurate, timely patient education is crucial for informed decision-making, especially regarding prostate-specific antigen screening and treatment options. However, the accuracy and reliability of AI chatbots' medical information must be rigorously evaluated. Studies testing ChatGPT's knowledge of prostate cancer are emerging, but there is a need for ongoing evaluation to ensure the quality and safety of information provided to patients. This study aims to evaluate the quality, accuracy, and readability of ChatGPT-4's responses to common prostate cancer questions posed by patients. Overall, 8 questions were formulated with an inductive approach based on information topics in peer-reviewed literature and Google Trends data. Adapted versions of the Patient Education Materials Assessment Tool for AI (PEMAT-AI), Global Quality Score, and DISCERN-AI tools were used by 4 independent reviewers to assess the quality of the AI responses. The 8 AI outputs were judged by 7 expert urologists, using an assessment framework developed to assess accuracy, safety, appropriateness, actionability, and effectiveness. The AI responses' readability was assessed using established algorithms (Flesch Reading Ease score, Gunning Fog Index, Flesch-Kincaid Grade Level, The Coleman-Liau Index, and Simple Measure of Gobbledygook [SMOG] Index). A brief tool (Reference Assessment AI [REF-AI]) was developed to analyze the references provided by AI outputs, assessing for reference hallucination, relevance, and quality of references. The PEMAT-AI understandability score was very good (mean 79.44%, SD 10.44%), the DISCERN-AI rating was scored as \"good\" quality (mean 13.88, SD 0.93), and the Global Quality Score was high (mean 4.46/5, SD 0.50). Natural Language Assessment Tool for AI had pooled mean accuracy of 3.96 (SD 0.91), safety of 4.32 (SD 0.86), appropriateness of 4.45 (SD 0.81), actionability of 4.05 (SD 1.15), and effectiveness of 4.09 (SD 0.98). The readability algorithm consensus was \"difficult to read\" (Flesch Reading Ease score mean 45.97, SD 8.69; Gunning Fog Index mean 14.55, SD 4.79), averaging an 11th-grade reading level, equivalent to 15- to 17-year-olds (Flesch-Kincaid Grade Level mean 12.12, SD 4.34; The Coleman-Liau Index mean 12.75, SD 1.98; SMOG Index mean 11.06, SD 3.20). REF-AI identified 2 reference hallucinations, while the majority (28/30, 93%) of references appropriately supplemented the text. Most references (26/30, 86%) were from reputable government organizations, while a handful were direct citations from scientific literature. Our analysis found that ChatGPT-4 provides generally good responses to common prostate cancer queries, making it a potentially valuable tool for patient education in prostate cancer care. Objective quality assessment tools indicated that the natural language processing outputs were generally reliable and appropriate, but there is room for improvement.

Background: Hand fracture management requires precise diagnostic accuracy and complex decision-making. Advances in artificial intelligence (AI) suggest that large language models (LLMs) may assist or even rival traditional clinical approaches. This study evaluates the effectiveness of ChatGPT-4o, DeepSeek-V3, and Gemini 1.5 in diagnosing and recommending treatment strategies for hand fractures compared to experienced surgeons. Methods: A retrospective analysis of 58 anonymized hand fracture cases was conducted. Clinical details, including fracture site, displacement, and soft-tissue involvement, were provided to the AI models, which generated management plans. Their recommendations were compared to actual surgeon decisions, assessing accuracy, precision, recall, and F1 score. Results: ChatGPT-4o demonstrated the highest accuracy (98.28%) and recall (91.74%), effectively identifying most correct interventions but occasionally proposing extraneous options (precision 58.48%). DeepSeek-V3 showed moderate accuracy (63.79%), with balanced precision (61.17%) and recall (57.89%), sometimes omitting correct treatments. Gemini 1.5 performed poorly (accuracy 18.97%), with low precision and recall, indicating substantial limitations in clinical decision support. Conclusions: AI models can enhance clinical workflows, particularly in radiographic interpretation and triage, but their limitations highlight the irreplaceable role of human expertise in complex hand trauma management. ChatGPT-4o demonstrated promising accuracy but requires refinement. Ethical concerns regarding AI-driven medical decisions, including bias and transparency, must be addressed before widespread clinical implementation.

The aim of this systematic review is to update and synthesize new evidence on BREAST-Q questionnaire's ability to reflect patient-reported outcomes in women who have undergone breast reconstruction surgery (BRS) following mastectomy. PubMed, Science Direct, Google Scholar, Cochrane CENTRAL, and Clincaltrial.gov were searched for relevant studies from January 2009 to September 2021. Any interventional or observational studies that used BREAST-Q to assess patient-reported outcomes in the assessment of BRS following mastectomy were included. A total of 42 studies were eligible for inclusion in the review. Three were randomized controlled trials and 39 were observational studies. Compared with pre-operative scores, there was an improvement in all BREAST-Q outcome domains following BRS including 'satisfaction with breasts', \"satisfaction with outcome\" \"psychosocial\", \"physical\", and \"sexual wellbeing\". Sexual well-being had the lowest BREAST-Q score both pre-and post-operatively (37.8-80.0 and 39.0-78.0, respectively). Autologous BRS reports higher satisfaction and overall wellbeing compared to implant-based BRS. BREAST-Q has a higher and narrow internal consistency of 0.81 to 0.96 compared with other patient-reported outcome measures (PROMs; EORTC-QLQ, FACT-B, BR-23, BCTOS). The BREAST-Q questionnaire is the only PROM which allows patients to reflect on their care, surgical outcomes, and satisfaction collectively. This review highlights the fact that BREAST-Q can effectively and reliably measure satisfaction and wellbeing of breast cancer patients after BRS. Comparatively, sexual wellbeing shows poorer outcomes following BRS and more longitudinal studies are necessary to understand the basis for these findings. Compared to other PROMs, BREAST-Q is reliable and specific to breast cancer surgery. Overall, BREAST-Q can help clinicians improve their quality of service, understand patient experiences, and may be used as an auditing tool for surgical outcomes.

The incidence of head and neck cutaneous squamous cell carcinoma (HNcSCC) is unevenly distributed between men and women. At present, the mechanism behind this disparity remains elusive. This study conducted a systematic review and meta-analysis of proportions to investigate the disparity between sexes for patients with HNcSCC. PubMed, Scopus, EMBASE, MEDLINE, Emcare and CINAHL were searched in November 2021 and June 2022 (N > 50, English, human), and studies which examined the association between sex and HNcSCC were included. Analysis was conducted using RStudio with data and forest plots displaying males as a proportion of total patients with HNcSCC. Two independent researchers performed study selection, data extraction, data analysis and risk of bias. Eighty-two studies (1948 to 2018) comprising approximately 186,000 participants (67% male, 33% female) from 29 countries were included. Significantly more males had HNcSCC overall (71%; CI: 67–74). Males were also significantly more affected by cSCC of the ear (92%; CI: 89–94), lip (74%; CI: 66–81), and eyelid (56%; CI: 51–62). This study found HNcSCC disproportionately affected males overall and across all subtypes. Improving our understanding of sex-specific mechanisms in HNcSCC will better inform our preventive, therapeutic and prognostic practices.

Background and Objectives: Dupuytren’s disease (DD) most commonly affects the palm and metacarpophalangeal/proximal interphalangeal joints; distal digital involvement at the distal interphalangeal joint (DIPJ) is uncommon and incompletely characterised. This systematic review summarises reported cases of DD involving the DIPJ, with or without proximal interphalangeal joint (PIPJ) involvement, focusing on clinical patterns, management, and reported outcomes. Materials and Methods: A systematic search of PubMed, Embase, and Scopus from database inception to July 2025 identified published case reports and case series describing DD confined to the DIPJ ± PIPJ of a single digit. Data were extracted on the demographics, digit involvement, anatomic features when reported, interventions, outcomes, complications, recurrence, and follow-up. Results: Nine studies reported 13 patients, published between 1991 and 2023. All patients were male (age range 25–79 years). The little finger predominated (10/13), followed by the ring (2/13) and index finger (1/13). When laterality was described, radial-sided distal cords were common. Surgical fasciectomy was performed in 11 cases and collagenase clostridium histolyticum (CCH) in 2 cases. Where postoperative correction was reported, outcomes were generally favourable; however, joint-specific range-of-motion outcomes and follow-up were inconsistently documented. Follow-up, when reported, ranged from 3 months to 3 years. One recurrence involving the PIPJ was reported 36 months after surgical management. No intraoperative neurovascular or tendon injuries were described, although adverse-event reporting was incomplete in some reports. Conclusions: Reported cases of DD involving the DIPJ most frequently involve the little finger in men. However, the available evidence is limited to a small number of selectively published case reports and series with incomplete outcome and follow-up reporting. These observations should therefore be interpreted cautiously, and comparative effectiveness or durability estimates cannot be established.

Purpose: Visual functioning evaluated by the Catquest-9SF questionnaire has shown to be a valid measure for assessing a patient's prioritization for cataract surgery. This study adapted Catquest-9SF for visual function outcomes post uni-lateral cataract surgery or bi-lateral cataract surgery. Methods: Visual functioning was assessed before and after uni-lateral or bi-lateral cataract surgery using the Catquest-9SF questionnaire. Patients were enrolled to this study prior to their cataract surgery between March 29 and April 30, 2021 at Shellharbour Hospital, Australia. Catquest-9SF questionnaires were completed prior to and 3 months post surgery. Resulting data were assessed for fit to a Rasch model using WINSTEPS software (version 4.2.0). Catquest-9SF data analysis of Chi-square, Wilcoxon sum test, and Fischer's test were performed in R (version 4.1.0). P value <.05 was considered statistically significant. Results: Sixty-one patients (mean age = 73.2 years, 62% female) were included for analysis. Catquest-9SF response thresholds, adequate precision (person separation index = 2.58, person reliability = 0.87, Cronbach's alpha = 0.74), uni-dimensionality, and no misfits (infit range 0.65-1.33; outfit range 0.64-1.31) were recorded. The mean of item calibration for patients was -0.22 post-operatively. There was significant (P <.05) improvement (16.3%) in visual functions across all nine Catquest 9-SF items. There was a significant mean visual function difference between patients with uni-lateral (10.1%) and bi-lateral cataract surgery (22.3%) pre-operatively and post-operatively. Conclusion: The Catquest-9SF questionnaire showed excellent psychometric properties and can assess visual functioning in an Australian population. There was a significant improvement in patient visual function post cataract surgery and higher functioning with bi-lateral cataract surgery.

Artificial intelligence (AI), notably Generative Adversarial Networks, has the potential to transform medical and patient education. Leveraging GANs in medical fields, especially cosmetic surgery, provides a plethora of benefits, including upholding patient confidentiality, ensuring broad exposure to diverse patient scenarios, and democratizing medical education. This study investigated the capacity of AI models, DALL-E 2, Midjourney, and Blue Willow, to generate realistic images pertinent to cosmetic surgery. We combined the generative powers of ChatGPT-4 and Google’s BARD with these GANs to produce images of various noses, faces, and eyelids. Four board-certified plastic surgeons evaluated the generated images, eliminating the need for real patient photographs. Notably, generated images predominantly showcased female faces with lighter skin tones, lacking representation of males, older women, and those with a body mass index above 20. The integration of AI in cosmetic surgery offers enhanced patient education and training but demands careful and ethical incorporation to ensure comprehensive representation and uphold medical standards.

Background Large language models (LLMs) are emerging artificial intelligence (AI) technologies refining research and healthcare. However, the impact of these models on presurgical planning and education remains under-explored. Objectives This study aims to assess 3 prominent LLMs—Google's AI BARD (Mountain View, CA), Bing AI (Microsoft, Redmond, WA), and ChatGPT-3.5 (Open AI, San Francisco, CA) in providing safe medical information for rhinoplasty. Methods Six questions regarding rhinoplasty were prompted to ChatGPT, BARD, and Bing AI. A Likert scale was used to evaluate these responses by a panel of Specialist Plastic and Reconstructive Surgeons with extensive experience in rhinoplasty. To measure reliability, the Flesch Reading Ease Score, the Flesch–Kincaid Grade Level, and the Coleman–Liau Index were used. The modified DISCERN score was chosen as the criterion for assessing suitability and reliability. A t test was performed to calculate the difference between the LLMs, and a double-sided P-value <.05 was considered statistically significant. Results In terms of reliability, BARD and ChatGPT demonstrated a significantly (P < .05) greater Flesch Reading Ease Score of 47.47 (±15.32) and 37.68 (±12.96), Flesch–Kincaid Grade Level of 9.7 (±3.12) and 10.15 (±1.84), and a Coleman–Liau Index of 10.83 (±2.14) and 12.17 (±1.17) than Bing AI. In terms of suitability, BARD (46.3 ± 2.8) demonstrated a significantly greater DISCERN score than ChatGPT and Bing AI. In terms of Likert score, ChatGPT and BARD demonstrated similar scores and were greater than Bing AI. Conclusions BARD delivered the most succinct and comprehensible information, followed by ChatGPT and Bing AI. Although these models demonstrate potential, challenges regarding their depth and specificity remain. Therefore, future research should aim to augment LLM performance through the integration of specialized databases and expert knowledge, while also refining their algorithms. Level of Evidence: 5

Breast reconstruction is an essential component in the multidisciplinary management of breast cancer patients. Over the years, preoperative planning has played a pivotal role in assisting surgeons in planning operative decisions prior to the day of surgery. The evolution of preoperative planning can be traced back to the introduction of modalities such as ultrasound and colour duplex ultrasonography, enabling surgeons to evaluate the donor site’s vasculature and thereby plan operations more accurately. However, the limitations of these techniques paved the way for the implementation of modern three-dimensional imaging technologies. With the advancements in 3D imaging, including computed tomography and magnetic resonance imaging, surgeons gained the ability to obtain detailed anatomical information. Moreover, numerous adjuncts have been developed to aid in the planning process. The integration of 3D-printing technologies has made significant contributions, enabling surgeons to create complex haptic models of the underlying anatomy. Direct infrared thermography provides a non-invasive, visual assessment of abdominal wall vascular physiology. Additionally, augmented reality technologies are poised to reshape surgical planning by providing an immersive and interactive environment for surgeons to visualize and manipulate 3D reconstructions. Still, the future of preoperative planning in breast reconstruction holds immense promise. Most recently, artificial intelligence algorithms, utilising machine learning and deep learning techniques, have the potential to automate and enhance preoperative planning processes. This review provides a comprehensive assessment of the history of innovation in preoperative planning for breast reconstruction, while also outlining key future directions, and the impact of artificial intelligence in this field.