Background Microsurgical free tissue transfer has become a reliable technique with success rates around 99% and around 5% requiring exploration for vascular compromise. Protocols for flap monitoring between plastic surgery units vary. We aimed to elucidate the time period when monitoring is crucial for flap salvage. Methods A systematic search of literature was performed in PubMed, Cochrane Library, Medline, and Scopus databases from 1966 to July 2018 according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, identifying 3,844 studies with mention of free flap and monitoring or timing or salvage or compromise. Studies were screened for relevance according to predetermined inclusion criteria. Data was extracted from included studies relating to flap type, monitoring, timing and reason for failure, and success of salvage intervention. Results A total of 109 studies featuring 44,031 free flaps were included. A total of 2,549 (5.8%) flaps required return to theater for compromise; 926 (2.1%) were lost and 1,654 (3.7%) were salvaged. In the first 24 hours postoperatively 93.8% of explored flaps are successfully salvaged, by day 2: 83.33%, day 3: 12.1%, and beyond day 4: none were successful. Of the 355 flaps where the cause of failure was reported, 59.5% was venous, 27.9% was arterial, 2.3% was a combination of both, and 10.2% was hematoma or infection. The proportion of flap failures at various recipient sites was highest in the trunk/viscera (7%, 95% confidence interval [CI] 0.00, 0.36), followed by limbs (5%, 95% CI 0.02, 0.08), head and neck (3%, 95% CI 0.02, 0.04), and breast (<1%; 95% CI 0.00, 0.02). Conclusion Close flap monitoring is of most value in the first 48 hours postoperatively, facilitating rapid detection of vascular compromise, early salvage, and better outcomes. The location of the flap has implications on its success and certain recipient sites may need particular attention to improve chances of success.

Background: Dupuytren’s fibroproliferative disease affecting the hand’s palmar fascia leads to progressive finger contractures and functional limitations. Management of this condition relies heavily on the expertise of hand surgeons, who tailor interventions based on clinical assessment. With the growing interest in artificial intelligence (AI) in medical decision-making, this study aims to evaluate the feasibility of integrating AI into the clinical management of Dupuytren’s disease by comparing AI-generated recommendations with those of expert hand surgeons. Methods: This multicentric comparative study involved three experienced hand surgeons and five AI systems (ChatGPT, Gemini, Perplexity, DeepSeek, and Copilot). Twenty-two standardized clinical prompts representing various Dupuytren’s disease scenarios were used to assess decision-making. Surgeons and AI systems provided management recommendations, which were analyzed for concordance, rationale, and predicted outcomes. Key metrics included union accuracy, surgeon agreement, precision, recall, and F1 scores. The study also evaluated AI performance in unanimous versus non-unanimous cases and inter-AI agreements. Results: Gemini and ChatGPT demonstrated the highest union accuracy (86.4% and 81.8%, respectively), while Copilot showed the lowest (40.9%). Surgeon agreement was highest for Gemini (45.5%) and ChatGPT (42.4%). AI systems performed better in unanimous cases (accuracy up to 92.0%) than in non-unanimous cases (accuracy as low as 35.0%). Inter-AI agreements ranged from 75.0% (ChatGPT-Gemini) to 48.0% (DeepSeek-Copilot). Precision, recall, and F1 scores were consistently higher for ChatGPT and Gemini than for other systems. Conclusions: AI systems, particularly Gemini and ChatGPT, show promise in aligning with expert surgical recommendations, especially in straightforward cases. However, significant variability exists, particularly in complex scenarios. AI should be viewed as complementary to clinical judgment, requiring further refinement and validation for integration into clinical practice.

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.

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: Thoracic air complications after autologous breast reconstruction (ABR) are uncommon and incompletely characterised. A personalised perioperative approach requires integration of patient anatomy, reconstructive technique, anaesthetic exposures and the postoperative clinical trajectory rather than reliance on population-level estimates alone. Methods: We report a 41-year-old woman in whom pneumomediastinum was identified after bilateral DIEP flap reconstruction and performed a focused literature review restricted to primary studies of pneumothorax or pneumomediastinum following autologous flap breast reconstruction. Searches across MEDLINE-Ovid, PubMed and Wiley Online Library using predefined MeSH terms were conducted. Data extraction focused on study characteristics, incidence, mechanism of injury, perioperative factors, and management. Results: The patient developed postoperative hypotension and persistent tachycardia, followed by pleuritic chest pain. CT excluded pulmonary embolism and oesophageal perforation but demonstrated Moderate volume pneumomediastinum without pneumothorax. She was managed conservatively and discharged on postoperative day 7. Five primary studies met the eligibility criteria. Study-specific pneumothorax frequencies were 1/463 (0.22%) after rib-sparing free-flap reconstruction, 3/749 (0.4%) after extended latissimus dorsi reconstruction, and 4/180 patients (2.2%; 1.4 per 100 internal mammary vessel dissections). Reported mechanisms included pleural injury during internal mammary vessel dissection, inadvertent puncture during regional anaesthesia, barotrauma under positive-pressure ventilation, and drain-related barotrauma. No previous primary report of pneumomediastinum after autologous flap breast reconstruction was identified. Conclusions: Pneumothorax and pneumomediastinum are uncommon but potentially serious complications of ABR. They are rarely documented postoperatively; however, surgeons and anaesthetists must remain vigilant, particularly during exposure of the internal mammary vessels and airway management. Patient-specific assessment, symptom-directed imaging and multidisciplinary management may support early recognition to prevent morbidity and preserve the outcomes of reconstructive procedures.