How I Do It: Using ChatGPT to Create Clinical Scenarios for Surgical Learning and Oral Boards Preparation.

OBJECTIVE: To evaluate the use of the large language model ChatGPT to simulate an oral surgical boards examination environment. DESIGN: ChatGPT was asked to create oral surgical boards questions based on a series of clinical scenarios. RESULTS: ChatGPT created clinically relevant oral board-type questions. ChatGPT provided pertinent follow-up questions after the user's response as would occur in an oral examination as well as feedback regarding the user's response. CONCLUSIONS: Chat GPT can simulate an oral boards-style examination of a surgical trainee with a reasonable degree of clinical detail and immediate feedback. It may be a useful as a curricular tool and for self-education and board preparation.

The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis.

Introduction: Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods: The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system. Results: 363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan-Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38-4.80), 2 points: 3.91 (2.11-7.24), and 3 points: 5.09 (2.70-9.58), p < 0.01). Conclusion: Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.