Forte, Antonio J.

Introduction The Care Hotel is a virtual medicine hybrid model of care implemented at the Mayo Clinic to increase use of enhanced recovery protocols, address hospital capacity and reduce medical costs. This voluntary accommodation offers patients a comfortable environment to receive nursing care postoperatively with in-person daytime monitoring and 24-hour virtual telemonitoring in a hotel setting. This study aims to evaluate postoperative outcomes in patients admitted to the Care Hotel after mastectomy with immediate tissue expander reconstruction compared to standard overnight admission. Methods A retrospective review of the electronic medical record was conducted for patients who underwent mastectomy with immediate tissue expander reconstruction prior to (1/2019–1/2020) and after (7/2021–7/2023) the implementation of the Care Hotel. Postoperative outcomes, including 48-hour ED visit, 48-hour readmission, and 30-day infection, hematoma, seroma, admission, and return to operating room were collected. Results There were 61 patients with 108 tissue expander reconstructions in the hospital admission group and 122 patients with 212 tissue expander reconstructions in the Care Hotel group. The average length of hospital stay was 1.2 days while the average Care Hotel stay was 1.4 days. There were four 48-hour ED visits with 1 admission for 12 hours of observation in the Care Hotel group. There was no statistically significant difference in 30-day infection, seroma, hematoma, readmission, need for IR drain procedure or return to operating room between the groups. Conclusions This investigation demonstrates the safety of postoperative virtual telemonitoring and should encourage providers to utilize virtual visit technology in the immediate postoperative time-period.

(1) Burn injuries demand multidisciplinary, evidence-based care, yet the extensive literature complicates timely decision making. Retrieval-augmented generation (RAG) synthesizes research while addressing inaccuracies in pretrained models. However, citation bias in sourcing for RAG often prioritizes highly cited studies, overlooking less-cited but valuable research. This study examines RAG’s performance in burn management, comparing citation levels to enhance evidence synthesis, reduce selection bias, and guide decisions. (2) Two burn management datasets were assembled: 30 highly cited (mean: 303) and 30 less-cited (mean: 21). The Gemini-1.0-Pro-002 RAG model addressed 30 questions, ranging from foundational principles to advanced surgical approaches. Responses were evaluated for accuracy (5-point scale), readability (Flesch–Kincaid metrics), and response time with Wilcoxon rank sum tests (p < 0.05). (3) RAG achieved comparable accuracy (4.6 vs. 4.2, p = 0.49), readability (Flesch Reading Ease: 42.8 vs. 46.5, p = 0.26; Grade Level: 9.9 vs. 9.5, p = 0.29), and response time (2.8 vs. 2.5 s, p = 0.39) for the highly and less-cited datasets. (4) Less-cited research performed similarly to highly cited sources. This equivalence broadens clinicians’ access to novel, diverse insights without sacrificing quality. As plastic surgery evolves, RAG’s inclusive approach fosters innovation, improves patient care, and reduces cognitive burden by integrating underutilized studies. Embracing RAG could propel the field toward dynamic, forward-thinking care.

Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus on Multimodal Large Language Models (MLLMs). We evaluated their ability to analyze facial skin quality, volume, symmetry, and adherence to aesthetic standards such as neoclassical facial canons and the golden ratio. Methods: We evaluated four MLLMs—ChatGPT-4o, ChatGPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet—using two evaluation forms and 15 diverse facial images generated by a Generative Adversarial Network (GAN). The general analysis form evaluated qualitative skin features (texture, type, thickness, wrinkling, photoaging, and overall symmetry). The facial ratios form assessed quantitative structural proportions, including division into equal fifths, adherence to the rule of thirds, and compatibility with the golden ratio. MLLM assessments were compared with evaluations from a plastic surgeon and manual measurements of facial ratios. Results: The MLLMs showed promise in analyzing qualitative features, but they struggled with precise quantitative measurements of facial ratios. Mean accuracy for general analysis were ChatGPT-4o (0.61 ± 0.49), Gemini 1.5 Pro (0.60 ± 0.49), ChatGPT-4 (0.57 ± 0.50), and Claude 3.5 Sonnet (0.52 ± 0.50). In facial ratio assessments, scores were lower, with Gemini 1.5 Pro achieving the highest mean accuracy (0.39 ± 0.49). Inter-rater reliability, based on Cohen’s Kappa values, ranged from poor to high for qualitative assessments (κ > 0.7 for some questions) but was generally poor (near or below zero) for quantitative assessments. Conclusions: Current general purpose MLLMs are not yet ready to replace manual clinical assessments but may assist in general facial feature analysis. These findings are based on testing models not specifically trained for facial analysis and serve to raise awareness among clinicians regarding the current capabilities and inherent limitations of readily available MLLMs in this specialized domain. This limitation may stem from challenges with spatial reasoning and fine-grained detail extraction, which are inherent limitations of current MLLMs. Future research should focus on enhancing the numerical accuracy and reliability of MLLMs for broader application in plastic surgery, potentially through improved training methods and integration with other AI technologies such as specialized computer vision algorithms for precise landmark detection and measurement.

Background: Immediate lymphatic reconstruction (ILR) via lymphovenous anastomosis is performed in patients undergoing axillary lymph node dissection (ALND) to minimize the risk of lymphedema. Following ALND, drains are placed to prevent seromas, while prolonged drain days are associated with increased risk of infection. The study aim was to determine the impact of ILR on the number of drain days following ALND in patients with breast cancer. Methods: We report a post-hoc analysis of a prospective trial (April 2018-January 2023) comparing patients with breast cancer who underwent ALND with ILR (intervention arm) vs ALND without ILR (control arm). Wilcoxon rank sum test and chi-squared or Fisher’s exact tests compared continuous and categorical variables, respectively. Multivariable quantile regression models evaluated the difference in drain days and days until drain removal criteria were met with covariates adjusted. Results: Data from 214 patients were analyzed (n = 115 interventional arm, n = 99 control arm). The intervention arm had a median of 15 axillary drain days compared to 11 days in the control arm ( P = .001). There was no significant difference between groups when measuring drain days based on the date the drain met removal criteria, rather than actual drain removal date. Seroma incidence was lower in the intervention arm (3.5%) vs the control arm (12.1%) ( P = .017) without a statistical difference in incidence of infection. Conclusion: ALND with ILR does not decrease the number of drain days compared to ALND without ILR in patients with breast cancer, although we found the incidence of seromas to be lower without increase in incidence of infection.

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, the rapid advancement of Generative Artificial Intelligence (GAI) models presents a new opportunity to automate and accelerate this process. This study evaluated the potential of GAI models to produce craniofacial anatomy illustrations for educational purposes. Methods: Four GAI models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, and Stable Diffusion 2.0 were used to generate 736 images across multiple views of surface anatomy, bones, muscles, blood vessels, and nerves of the cranium in both oil painting and realistic photograph styles. Four reviewers evaluated the images for anatomical detail, aesthetic quality, usability, and cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: Midjourney v6.0 scored highest for aesthetic quality and cost-effectiveness, and DALL-E 3 performed best for anatomical detail and usability. The inter-rater reliability analysis demonstrated a high level of agreement among reviewers (ICC = 0.858, 95% CI). However, all models showed significant flaws in depicting crucial anatomical details such as foramina, suture lines, muscular origins/insertions, and neurovascular structures. These limitations were further characterized by abstract depictions, mixing of layers, shadowing, abnormal muscle arrangements, and labeling errors. Conclusions: These findings highlight GAI’s potential for rapidly creating craniofacial anatomy illustrations but also its current limitations due to inadequate training data and incomplete understanding of complex anatomy. Refining these models through precise training data and expert feedback is vital. Ethical considerations, such as potential biases, copyright challenges, and the risks of propagating inaccurate information, must also be carefully navigated. Further refinement of GAI models and ethical safeguards are essential for safe use.

Artificial intelligence (AI) is not merely a tool for the future of clinical medicine; it is already reshaping the landscape, challenging traditional paradigms, and expanding the horizons of what is achievable in healthcare [...]