Navigating the Ethical Landmines of ChatGPT: Implications of Intelligent Chatbots in Plastic Surgery Clinical Practice.

ChatGPT is a cutting-edge language model developed by OpenAI with the potential to impact all facets of plastic surgery from research to clinical practice. New applications for ChatGPT are emerging at a rapid pace in both the scientific literature and popular media. It is important for clinicians to understand the capabilities and limitations of these tools before patient-facing implementation. In this article, the authors explore some of the technical details behind ChatGPT: what it is, and what it is not. As with any emerging technology, attention should be given to the ethical and health equity implications of this technology on our plastic surgery patients. The authors explore these concerns within the framework of the foundational principles of biomedical ethics: patient autonomy, nonmaleficence, beneficence, and justice. ChatGPT and similar intelligent conversation agents have incredible promise in the field of plastic surgery but should be used cautiously and sparingly in their current form. To protect patients, it is imperative that societal guidelines for the safe use of this rapidly developing technology are developed.

Air or Saline? A Propensity Score-Matched Analysis on the Effect of Tissue Expander Fill on Complications in Immediate Breast Reconstruction.

BACKGROUND: Tissue expander fill medium and volume have implications for the pressure exerted on mastectomy skin flaps. This study evaluated the influence of initial fill medium (air vs. saline) on complications in immediate breast reconstruction within a propensity score-matched cohort. PATIENTS AND METHODS: Patients undergoing immediate tissue expander-based breast reconstruction with initial intraoperative fill with air were propensity score matched 1:2 to those with saline initial fill based on patient and tissue expander characteristics. Incidence of overall and ischemic complications were compared by fill medium (air vs. saline). RESULTS: A total of 584 patients were included, including 130 (22.2%) with initial fill with air, 377 (64.6%) with initial fill with saline, and 77 (13.2%) with 0 cc of initial fill. After multivariate adjustment, higher intraoperative fill volume was associated with increased risk of mastectomy skin flap necrosis [regression coefficient (RC) 15.7; p = 0.049]. Propensity score matching was then conducted among 360 patients (Air: 120 patients vs. Saline: 240 patients). After propensity score matching, there were no significant differences in the incidences of mastectomy skin flap necrosis, extrusion, reoperation, or readmission between the air and saline cohorts (all p > 0.05). However, initial fill with air was associated with lower incidence of infection requiring oral antibiotics (p = 0.003), seroma (p = 0.004), and nipple necrosis (p = 0.03). CONCLUSIONS: Within a propensity score-matched cohort, initial fill with air was associated with a lower incidence of complications, including ischemic complications after nipple-sparing mastectomy. Initial fill with air and lower fill volumes may be strategies to reducing risk of ischemic complications among high-risk patients.

Two Thumbs Down: A 10-Year Analysis of Crossbow-Related Hand and Digit Injuries in the United States.

BACKGROUND: Modern crossbow is a popular weapon used throughout the United States for outdoor recreation. Crossbow mechanics place the hand and digits at significant risk of injury while shooting or handling these weapons; however, injury patterns have been poorly documented. In this study, the authors use a national database to assess patterns of crossbow-related injuries of the hand and digits. METHODS: A retrospective review of the National Electronic Injury Surveillance System database was conducted to identify crossbow-related hand and digit injuries over the past 10 years. Demographic data, injury timing, anatomical injury location, specific injury diagnosis, and disposition data were collected. RESULTS: A total of 15 460 crossbow-related hand injuries were identified between 2011 and 2021. A strong temporal relationship was identified, with 89% of injuries occurring between the months of August and December. Male patients sustained most injuries (>85%). Body areas injured were the digits (93.2%) and the hand (5.7%). The most common injuries were laceration (n = 7520, 48.6%), fracture (n = 4442, 28.7%), amputation (n = 1341, 8.7%), and contusion/abrasion (n = 957, 6.2%). More than 50% of cases involved injury to the thumb, with approximately 750 thumb amputations recorded during the time period examined. CONCLUSIONS: This study is the first to document the patterns of hand and digit injuries associated with crossbow use on a national scale. These findings have important implications for public health awareness campaigns targeting hunters and support the designation of crossbow "safety wings" as a mandatory feature of crossbow design.

Predicting Final Implant Volume in Two-stage Prepectoral Breast Reconstruction.

Two-stage implant-based breast reconstruction remains the most commonly performed reconstructive modality following mastectomy. Although prior studies have explored the relationship between tissue expander (TE) features and permanent implant (PI) size in subpectoral reconstruction, no such study exists in prepectoral reconstruction. This study aims to identify pertinent TE characteristics and evaluate their correlations with PI size for prepectoral implant-based reconstruction. Methods: This study analyzed patients who underwent two-stage prepectoral tissue expansion for breast reconstruction followed by implant placement. Patient demographics and oncologic characteristics were recorded. TE and PI features were evaluated. Significant predictors for PI volume were identified using linear and multivariate regression analyses. Results: We identified 177 patients and 296 breast reconstructions that met inclusion criteria. All reconstructions were performed in the prepectoral plane with the majority using acellular dermal matrix (93.8%) and primarily silicone implants (94.3%). Mean TE size was 485.4 cm3 with mean initial fill of 245.8 cm3 and mean final fill of 454.4 cm3. Mean PI size was 502.9 cm3 with a differential fill volume (PI-TE) of 11.7 cm3. Multivariate analysis identified significant features for PI size prediction, including TE size (R2 = 0.60; P < 0.0001) and TE final fill volume (R2 = 0.57; P < 0.0001). The prediction expression for TE final fill and TE size was calculated as 26.6 + 0.38*(TE final fill) + 0.61*(TE size). Conclusions: TE size and final expansion volume were significant variables for implant size prediction. With prepectoral implant placement gaining popularity, the predictive formula may help optimize preoperative planning and decision-making in prepectoral reconstructions.

Deep Learning Applications in Surgery: Current Uses and Future Directions.

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.

Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery.

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.

New Frontiers of Natural Language Processing in Surgery.

The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions. We identify current perioperative applications of NLP algorithms as well as research limitations and future avenues to outline the impact and potential of this technology for progressing surgical innovation.