Our aging population, diabetes, and obesity have fueled the growth of chronic wounds seen throughout the world. Often, wounds are a marker of poor health that leads to increased mortality rates. However, the diagnosis and treatment of these wounds are challenging. Incorrectly differentiating between chronic wounds and other complex conditions can lead to adverse events. Artificial intelligence (AI) has been shown to offer some early benefits, and we hypothesized that it may enhance wound care but also carry some notable risks. We performed a detailed search using PubMed, Scopus, Cumulated Index in Nursing and Allied Health Literature, and Web of Science for AI applications in wound care. AI was found to be applied to wound diagnosis and characterization, wound monitoring for tissue change, daily therapy, and prevention and prognostics. AI made for more efficient and accurate wound assessments, less painful assessments of chronic wounds, more personalized treatment, and improved prognostic prediction capabilities. AI also allowed for more precise at-home observation and care, facilitating earlier wound treatment as needed. Challenges associated with AI included how to best allocate AI-assisted technologies equitably, how to safely maintain patient data, and how to diversify datasets for algorithm training. Because the algorithms are not transparent, validating findings may be challenging. AI presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care. More research needs to be done into how to best incorporate AI into daily clinical practice while keeping clinicians aware of the potential risks of using these evolving technologies.

In the rapidly advancing landscape of surgical education, the traditional apprenticeship model is being increasingly complemented by individualized learning, competency-based assessment, and data-driven feedback. Work-hour restrictions, administrative burdens, and limited operative exposure have intensified the need for innovative solutions to supplement faculty-led training. Artificial intelligence (AI) has emerged as a promising adjunct, offering scalable platforms for technical skill acquisition, personalized feedback, and structured progress tracking. Early applications include AI-guided simulation, feedback, natural language processing for resident evaluation, and advanced applicant-screening systems, which hold the potential to streamline holistic review while reducing faculty workload. Despite these advances, significant challenges remain, including bias mitigation, ethical data governance, and the need for rigorous outcome-based validation. The greatest promise lies in hybrid models, where AI augments rather than replaces mentorship, freeing faculty for complex, context-dependent teaching. With careful implementation, AI is poised to meaningfully transform surgical education worldwide.

Machine learning (ML), a branch of artificial intelligence, is rapidly transforming surgical complication and outcome prediction. Unlike traditional statistical approaches, ML can learn complex, nonlinear relationships across multiple variables, enabling more accurate and adaptable prognostication. Emerging ML-based tools have demonstrated strong performance across diverse surgical specialties, often surpassing conventional risk models. However, challenges remain, including opaque “black box” outputs, diminished performance during external validation, difficulty modeling rare events, and dependence on tabular data. These limitations can be mitigated but demand thoughtful design and rigorous validation. Importantly, ML introduces distinct methodological considerations unfamiliar to many surgeons. Successful clinical integration requires robust external validation and transparent sharing of trained models to ensure reproducibility and generalizability across diverse cohorts. By enhancing the precision of risk prediction, ML holds the potential to guide patient selection, optimize perioperative care, and strengthen shared decision-making between patients and surgeons.

Artificial intelligence (AI) has rapidly emerged as a transformative force in surgical research, driving innovation in preoperative planning, intraoperative guidance, workflow optimization, and academic productivity. Advances in computational power and multimodal data have expanded applications beyond retrospective analytics and predictive modeling to real-time clinical decision support. Current use cases span AI-assisted three-dimensional planning, automated scheduling, and ambient scribe technology. Simultaneously, large language models are increasingly applied to literature synthesis, data analysis, and manuscript development. Yet, this rapid integration raises critical ethical concerns, including algorithmic bias, data privacy, model opacity, and issues of accountability and generalizability. This review synthesizes recent technological advancements and examines these ethical challenges, proposing structured frameworks for responsible implementation. By embedding principles of transparency, equity, and patient safety, the surgical research community can ensure AI innovation translates into meaningful and trustworthy improvements in care.

Recent decades have seen a rapid advancement in nonsurgical facial rejuvenation techniques due to technological advances and growing patient preference for minimally invasive aesthetic procedures. Laser resurfacing and chemical peels are two popular modalities that address aging skin, improve skin texture, and reduce signs of photodamage. In this work, we examine the historical evolution of these modalities, review current trends, and analyze their comparative efficacy in the context of facial rejuvenation. We discuss each modalities’ mechanisms, clinical indications, efficacy, and safety profiles. We additionally explore the impact of emerging technologies, such as fractional lasers, picosecond lasers, and novel chemical peel formulations, on patient outcomes, recovery times, and novel indications. Furthermore, we consider how recent advances have enabled safer and more effective treatment across diverse skin types, focusing primarily on higher Fitzpatrick skin. Additionally, a scoping review including adjunctive and non-surgical modalities is discussed and synthesized to highlight current evidence, clinical guidelines, and technological advances. This review aims to guide clinicians in optimizing procedure choice and patient outcomes in nonsurgical facial rejuvenation.

Autologous breast reconstruction is technically demanding, and intraoperative shaping of the tissue flap into a three-dimensional breast remains a time-con