Salehinejad, Hojjat

ImportanceThis review aims to assess the benefits and risks of implementing large language model (LLM) solutions in an academic surgical setting.ObservationsThe integration of LLMs and artificial intelligence (AI) into surgical practice has generated international attention with the emergence of OpenAI’s ChatGPT and Google’s Bard. From an administrative standpoint, LLMs have the potential to revolutionize academic practices by reducing administrative burdens and improving efficiency. LLMs have the potential to facilitate surgical research by increasing writing efficiency, building predictive models, and aiding in large dataset analysis. From a clinical standpoint, LLMs can enhance efficiency by triaging patient concerns and generating automated responses. However, challenges exist, such as the need for improved LLM generalization performance, validating content, and addressing ethical concerns. In addition, patient privacy, potential bias in training, and legal responsibility are important considerations that require attention. Research and precautionary measures are necessary to ensure safe and unbiased use of LLMs in surgery.Conclusions and RelevanceAlthough limitations exist, LLMs hold promise for enhancing surgical efficiency while still prioritizing patient care. The authors recommend that the academic surgical community further investigate the potential applications of LLMs while being cautious about potential harms.

Abstract The scarcity of organ donors relative to the number of patients with End Stage Kidney Disease (ESKD) has led to prolonged waiting times for kidney transplants, contributing to elevated cardiovascular mortality risk. Transplant professionals are tasked with the complex allocation of limited organs to a vulnerable patient group facing heightened morbidity and mortality risk. The need for continuous re-evaluation of waitlisted patients is evident due to the significant number who perish while awaiting transplantation. Among individuals with ESKD, vascular calcification, particularly Abdominal Arterial Calcifications (AAC), holds predictive value for cardiovascular events and mortality. However, a standardized method to quantify AAC's prognostic potential remains lacking, especially for kidney transplant evaluations. This study presents an ensemble machine learning (ML) approach to study the relationship between AAC score and mortality in patients on the waitlist and triage patients needing transplantation. Using the AAC score, the proposed ML model can predict kidney transplant waitlist morality with an accuracy of 78% while its accuracy is 68% without using this score. This study leverages explainable ML to explore the relationship between predictors and mortality in waitlisted patients, aiming to improve patient triage accuracy.