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.

Fournier’s gangrene (FG) is a rare form of necrotizing fasciitis of the perineal, genital, or perianal region. It is characterized by an aggressive course and high mortality rate, over 20%. FG demands immediate treatment including resuscitation maneuvers, intravenous antibiotic therapy and early surgical debridement. Background/Objectives: The gold-standard treatment for FG is surgical reconstruction. However, up to date, no precise guidelines exist. Thus, we decided to systematically review the literature, focusing on FG contemporary approaches to reconstructive surgery, aiming to analyze the various reconstructive strategies and their specific indications. Methods: A systematic review was carried out according to the PRISMA statement by searching various databases from April 2014 to April 2024, using the terms ‘‘Fournier Gangrene OR Fournier Gangrene Reconstruction OR Fournier Gangrene Treatment OR Fournier Gangrene Plastic Surgery OR Necrotizing Fasciitis OR Necrotizing Fasciitis AND Reconstruction”. The eligibility criteria included original studies aimed at discussing FG reconstruction with at least three clinical cases. Results: The final synthesis included 38 articles, and 576 reconstructions were described. Of these, 77.6% were minimally invasive strategies (direct closure, secondary healing, grafts, and local random flaps), while more invasive reconstructions (loco-regional flaps based on known vascular anatomy) were adopted in 22.4%. No free flaps were reported. Conclusions: FG requires immediate medical interventions including broad-spectrum antibiotic therapy, surgical debridement, adjuvant therapies, and reconstructive surgeries. Taking into account the anatomical characteristics of the inguinal-crural region, skin grafts and local random flaps could offer versatile and effective reconstructions for most FG cases, while the more invasive strategies should be reserved for very few cases. Future research is warranted to define an FG dedicated reconstruction protocol.

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: Microsurgery is a highly complex and technically demanding field within reconstructive surgery, with outcomes heavily dependent on meticulous planning, precision, and postoperative monitoring. Over the last five years, artificial intelligence (AI) has emerged as a transformative tool across all phases of microsurgical care, offering new capabilities in imaging analysis, intraoperative decision support, and outcome prediction. Methods: A comprehensive narrative review was conducted to evaluate the peer-reviewed literature published between 2020 and May 2025. Multiple databases, including PubMed, Embase, Cochrane, Scopus, and Web of Science, were searched using combinations of controlled vocabulary and free-text terms relating to AI and microsurgery. Studies were included if they described AI applications during the preoperative, intraoperative, or postoperative phases of microsurgical care in human subjects. Discussion: Using predictive models, AI demonstrated significant utility in preoperative planning through automated perforator mapping, flap design, and individualised risk stratification. AI-enhanced augmented reality and perfusion analysis tools improved precision intraoperatively, while innovative robotic platforms and intraoperative advisors showed early promise. Postoperatively, mobile-based deep learning applications enabled continuous flap monitoring with sensitivities exceeding 90%, and AI models accurately predicted surgical site infections, transfusion needs, and long-term outcomes. Despite these advances, most studies relied on retrospective single-centre data, and large-scale, prospective validation remains limited. Conclusions: AI is poised to enhance microsurgical precision, safety, and efficiency. However, its integration is challenged by data heterogeneity, generalisability concerns, and the need for human oversight in nuanced clinical scenarios. Standardised data collection and multicentre collaboration are vital for robust, equitable AI deployment. With careful validation and implementation, AI holds the potential to redefine microsurgical workflows and improve patient outcomes across diverse clinical settings.

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.