Background/Objectives: Robotic systems offer enhanced precision, dexterity, and visualization, which are essential in addressing the complex nature of plastic surgery procedures. Despite widespread adoption in other surgical specialties, such as urology and gynecology, their application in plastic surgery remains underexplored. This review examines the use of robotic systems in plastic and reconstructive surgery with a focus on clinical outcomes. Methods: A literature search was conducted using PubMed, Embase, Scopus, and Web of Science. Search terms included (“robotic surgery” OR “surgical robots”) AND (“plastic surgery” OR “reconstructive surgery”). Studies on clinical outcomes and biomimetic innovations published between 1980 and 2024 were included, while non-English, cadaver-based, and animal studies were excluded. Data were systematically extracted using Covidence and analyzed. Results: Twenty-nine studies were identified that evaluated the clinical outcomes of robotics in areas including breast reconstruction, microsurgery, and craniofacial procedures. Robotic systems like the Da Vinci and Symani platforms offer motion scaling, tremor elimination, and enhanced depth perception. In nipple-sparing mastectomies, they reduced skin necrosis rates from 8% to 2%, while in DIEP flap reconstruction, they enabled smaller fascial incisions (2.67 ± 1.13 cm vs. 8.14 ± 1.69 cm) and faster recovery with fewer complications. In microsurgery, they achieved 100% patency for vessels under 0.3 mm and a 25.2% limb volume reduction in lymphedema patients in 3 months. Conclusions: Robotic systems show significant promise, particularly in procedures such as nipple-sparing mastectomies, and have the potential to overcome challenges including surgeon fatigue. However, challenges such as longer operating times, high costs, and limited haptic feedback remain barriers to their adoption.

Background: Artificial intelligence (AI) and large language models (LLMs) are increasingly used in healthcare, with applications in clinical decision-making and workflow optimization. In head and neck surgery, postoperative rehabilitation is a complex, multidisciplinary process requiring personalized care. This study evaluates the feasibility of using LLMs to generate tailored rehabilitation programs for patients undergoing major head and neck surgical procedures. Methods: Ten hypothetical head and neck surgical clinical scenarios were developed, representing oncologic resections with complex reconstructions. Four LLMs, ChatGPT-4o, DeepSeek V3, Gemini 2, and Copilot, were prompted with identical queries to generate rehabilitation plans. Three senior clinicians independently assessed their quality, accuracy, and clinical relevance using a five-point Likert scale. Readability and quality metrics, including the DISCERN score, Flesch Reading Ease, Flesch–Kincaid Grade Level, and Coleman–Liau Index, were applied. Results: ChatGPT-4o achieved the highest clinical relevance (Likert mean of 4.90 ± 0.32), followed by DeepSeek V3 (4.00 ± 0.82) and Gemini 2 (3.90 ± 0.74), while Copilot underperformed (2.70 ± 0.82). Gemini 2 produced the most readable content. A statistical analysis confirmed significant differences across the models (p < 0.001). Conclusions: LLMs can generate rehabilitation programs with varying quality and readability. ChatGPT-4o produced the most clinically relevant plans, while Gemini 2 generated more readable content. AI-generated rehabilitation plans may complement existing protocols, but further clinical validation is necessary to assess their impact on patient outcomes.

Background: Burn injuries require accurate assessment for effective management, and artificial intelligence (AI) is gaining attention in burn care for diagnosis, treatment planning, and decision support. This study compares the effectiveness of AI-driven models with experienced plastic surgeons in burn assessment and management. Methods: Ten anonymized burn images of varying severity and anatomical location were selected from publicly available databases. Three AI systems (ChatGPT-4o, Claude, and Kimi AI) analyzed these images, generating clinical descriptions and management plans. Three experienced plastic surgeons reviewed the same images to establish a clinical reference standard and evaluated AI-generated recommendations using a five-point Likert scale for accuracy, relevance, and appropriateness. Statistical analyses, including Cohen’s kappa coefficient, assessed inter-rater reliability and comparative accuracy. Results: AI models showed high diagnostic agreement with clinicians, with ChatGPT-4o achieving the highest Likert ratings. However, treatment recommendations varied in specificity, occasionally lacking individualized considerations. Readability scores indicated that AI-generated outputs were more comprehensible than the traditional medical literature, though some recommendations were overly simplistic. Cohen’s kappa coefficient suggested moderate to high inter-rater agreement among human evaluators. Conclusions: While AI-driven models demonstrate strong diagnostic accuracy and readability, further refinements are needed to improve treatment specificity and personalization. This study highlights AI’s potential as a supplementary tool in burn management while emphasizing the need for clinical oversight to ensure safe and individualized patient care.

Background: Large language models (LLMs) such as ChatGPT have evolved rapidly, with notable improvements in coherence, factual accuracy, and contextual relevance. However, their academic and clinical applicability remains under scrutiny. This study evaluates the temporal performance evolution of LLMs by comparing earlier model outputs (GPT-3.5 and GPT-4.0) with ChatGPT-4.5 across three domains: aesthetic surgery counseling, an academic discussion base of thumb arthritis, and a systematic literature review. Methods: We replicated the methodologies of three previously published studies using identical prompts in ChatGPT-4.5. Each output was assessed against its predecessor using a nine-domain Likert-based rubric measuring factual accuracy, completeness, reference quality, clarity, clinical insight, scientific reasoning, bias avoidance, utility, and interactivity. Expert reviewers in plastic and reconstructive surgery independently scored and compared model outputs across versions. Results: ChatGPT-4.5 outperformed earlier versions across all domains. Reference quality improved most significantly (a score increase of +4.5), followed by factual accuracy (+2.5), scientific reasoning (+2.5), and utility (+2.5). In aesthetic surgery counseling, GPT-3.5 produced generic responses lacking clinical detail, whereas ChatGPT-4.5 offered tailored, structured, and psychologically sensitive advice. In academic writing, ChatGPT-4.5 eliminated reference hallucination, correctly applied evidence hierarchies, and demonstrated advanced reasoning. In the literature review, recall remained suboptimal, but precision, citation accuracy, and contextual depth improved substantially. Conclusion: ChatGPT-4.5 represents a major step forward in LLM capability, particularly in generating trustworthy academic and clinical content. While not yet suitable as a standalone decision-making tool, its outputs now support research planning and early-stage manuscript preparation. Persistent limitations include information recall and interpretive flexibility. Continued validation is essential to ensure ethical, effective use in scientific workflows.

Trauma, particularly surgical trauma, has been suggested as a potential trigger for Dupuytren’s disease (DD). This systematic review examined the prevalence of DD after surgical and non-surgical hand trauma by conducting thorough searches of the PubMed, Embase, Cochrane and Scopus databases. Qualitative methods were used to synthesise the data and summarize the findings that were unsuitable for meta-analysis. The findings revealed an increased risk of DD associated with exposure to hand-transmitted vibration, sports-related trauma and distal radial fractures. Surgical procedures such as trigger finger release were also found to be associated with an increased risk, particularly in individuals who are predisposed to the condition. Key risk factors included age, male sex, diabetes and smoking. Hand trauma, particularly surgical trauma and repetitive mechanical stress, is associated with the onset of DD. These findings highlight the need to consider the risk of developing DD in patients undergoing surgery or sustaining an injury. Further research is needed to develop preventive strategies for at-risk populations.