ChatGPT, a large language model (LLM), was released on November 30, 2022. This model can generate seemingly intelligent writing by comprehensively answering prompts after being trained on vast amounts of text data¹. Its disruptive potential quickly caught the public’s attention, leading to many using it for writing tasks. As a result, media outlets have provided extensive coverage of ChatGPT and other LLMs, sparking discussions about their potential uses and controversies^2,3. While some view LLMs as valuable tools that could revolutionize science, others express scepticism and concern. However, their potential impact on surgical science remains unexplored. In this article, the potential role of LLMs like ChatGPT in surgical science, from the authors’ perspective as surgical scientists involved in both clinical trials and artificial intelligence-based surgical research, will be examined.

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Background: Histologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is used to assess the effect of NAT and may guide the choice for adjuvant treatment. However, evaluating residual tumor burden in pancreatic cancer is challenging given tumor response heterogeneity and challenging histomorphology. Artificial intelligence techniques may offer a more reproducible approach. Methods: From 64 patients, one H&E-stained slide of resected pancreatic cancer after NAT was digitized. Three separate classes were manually outlined in each slide (i.e., tumor, normal ducts, and remaining epithelium). Corresponding segmentation masks and patches were generated and distributed over training, validation, and test sets. Modified U-nets with varying encoders were trained, and F1 scores were obtained to express segmentation accuracy. Results: The highest mean segmentation accuracy was obtained using modified U-nets with a DenseNet161 encoder. Tumor tissue was segmented with a high mean F1 score of 0.86, while the overall multiclass average F1 score was 0.82. Conclusions: This study shows that artificial intelligence-based assessment of residual tumor burden is feasible given the promising obtained F1 scores for tumor segmentation. This model could be developed into a tool for the objective evaluation of the response to NAT and may potentially guide the choice for adjuvant treatment.

This systematic review provides the most comprehensive overview of intrahepatic biliary anatomical variants to date. Additionally, it proposes a clinically focused, prevalence-based classification system based on meta-analysis of a large pooled dataset whilst incorporating the merits of previous classification systems.