Surgery

Evaluating GPT-4 Performance on Plastic Surgery Oral Examination Vignettes: A Quantitative and Qualitative Analysis.

BACKGROUND: The use of artificial intelligence (AI) in medicine is rapidly evolving. However, its role in plastic and reconstructive surgery remains underexplored. Plastic surgery requires nuanced, dynamic decision making, and individualized care, making AI integration challenging. This study evaluates GPT-4's ability to respond to American Board of Plastic Surgery (ABPS) Oral Board-style clinical...

Enhancing readability and understandability of vascular surgery discharge summaries using artificial intelligence.

ObjectivesThis study aimed to assess whether Large Language Models (LLMs), like ChatGPT-4, could simplify discharge summaries for vascular surgery patients while maintaining accuracy and completeness, ultimately improving patient comprehension and engagement in their postoperative care.MethodsIn this cross-sectional multicentric study, discharge summaries from 90 vascular surgery patients across t...

Evaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms.

BACKGROUND AND PURPOSE: Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpin...

Multimodal AI System for Plastic Surgery Diagnosis and Decision-Making Using Deep Learning of Psychological Questionnaires and Three-Dimensional Facial Data.

PURPOSE: To design and develop an AI-based plastic surgery recommendation system using 3D photographs and psychological questionnaire surveys, aiming ...

Individualized gamma knife radiosurgery prescription dosing for pituitary adenomas: development and internal validation of a feedforward neural network model.

BACKGROUND: Gamma Knife radiosurgery (GKRS) is an established treatment for pituitary adenomas yet prescription dose selection is often guided by clin...

Advances in the postoperative care of the liver transplant recipient.

PURPOSE OF REVIEW: Survival rates following liver transplantation now exceed 90% at one year. However, the patient group undergoing liver transplantat...

Surface-enhanced Raman spectroscopy of serum exosomes coupled with support vector machine for diagnosis of Parkinson's disease.

Parkinson's disease (PD) diagnosis faces substantial challenges due to the lack of reliable biomarkers and the limitations of existing detection techn...

Deterministic Evolution of Aptamers via a Microfluidic-Integrated Robotic Platform Using Complex Exosomes as Targets.

Exosome (EXO) membrane proteins are attractive biomarkers for liquid biopsy, yet their heterogeneity makes it difficult to develop reliable antibody-b...

Dual Framework for Classification and Detection of Third Molar Impaction in Panoramic Radiographs.

BACKGROUND: The surgical extraction of impacted mandibular third molars present significant clinical challenges, where accurate preoperative assessmen...

Commercial AI platforms improve reproducibility but fail to meet clinical precision thresholds for intracranial aneurysm measurement: implications for serial surveillance.

BACKGROUND: Intracranial aneurysm affect 3-7% of the global population, with rupture causing > 80% of non-traumatic subarachnoid hemorrhage and approx...

AI-driven 3D virtual surgical planning in total hip arthroplasty: a machine learning approach for precision implant positioning and improved clinical outcomes.

PURPOSE: To explore the clinical significance of the artificial intelligence (AI)-assisted three-dimensional (3D) planning system AI-HIP in total hip ...

Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.

AIM: To map and critically appraise the current literature on Artificial Intelligence (AI) applications in emergency general surgery, with a focus on ...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mortality up to a year after hospital discharge. Impro...

Development and validation of interpretable machine learning models for predicting long-term functional outcomes in elderly patients with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Accurate prognostication in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH) remains challenging due to high morbidity and ...

Auditory and Vestibular Recovery in Otosclerosis Surgery: Clinical Efficacy and Predictive Modeling of Intraoperative Corticosteroid Use.

OBJECTIVE: To evaluate the effect of intraoperative local corticosteroid application on hearing, tinnitus, and dizziness outcomes after stapes surgery...

Predicting the surgical difficulty of laparoscopic cholecystectomy after percutaneous transhepatic gallbladder drainage in patients with acute cholecystitis: a multi-center study.

Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: A cross-sectional SHAP-based analysis of NHANES 2009-2023.

Depression (DEP) is a common yet underdiagnosed comorbidity in adults with type 2 diabetes mellitus (T2DM), worsening glycemic control and increasing ...

Design and rationale of the artifiCiAl intelligence Model for Evaluating the surgical techniques of caRdiAc surgeons (CAMERA): a cohort study.

INTRODUCTION: Coronary artery bypass grafting (CABG) is a technically demanding procedure where surgical skill directly influences outcomes. Tradition...

Intraoperative biopsy imaging of lung cancer risk.

BACKGROUND: Before surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy ...

Clinical-Radiomics hybrid prediction of the Risks of pedicle screw loosening after posterior lumbar fusion.

OBJECTIVE: We aimed to construct a risk prediction model for PSL after posterior lumbar fusion using machine learning and radiomic methods. SUMMARY OF...