Background/Objectives: Acute pancreatitis (AP) is an illness that requires prompt diagnosis and treatment since it has the potential to become life-threatening. The American College of Gastroenterology 2024 (ACG24) guidelines offer a framework for diagnosis, severity, and treatment criteria. To assess Google Gemini application of ACG24 guidelines to Medical Information Mart for Intensive Care-III AP cases for risk, nutrition, and complication management. Methods: This observational cross-sectional study was based on 512 patients with AP who were treated in the Medical Information Mart for Intensive Care-III database from 2001 to 2012. The study compared the efficiency of Gemini in relation to the ACG24 guidelines in the three main areas of risk stratification, enteral nutrition timing, and necrotizing pancreatitis management. Enteral nutrition, according to the ACG24 guidelines, should be started within 48 h for patients who are capable, and antibiotics should only be used for confirmed infected necrosis. Results: The study included 512 patients who were divided into two groups: 213 patients with mild pancreatitis (41.6%) and 299 patients with severe pancreatitis (58.4%). The model achieved 85% accuracy for mild cases and 82% accuracy for severe cases of pancreatitis. The Acute Physiology and Chronic Health Evaluation II and Ranson scores matched the predictions of Gemini for both mild cases (p = 0.28 and p = 0.33, respectively) and severe cases (p = 0.31 and p = 0.27, respectively). The recommendations for early enteral nutrition and delayed feeding in mild cases were correct for 78% of patients, but the system suggested oral intake prematurely in 8% of severe cases. The antibiotic guideline compliance reached 82% among 156 patients with necrotizing pancreatitis, and the procedure for draining infected necrosis was correct 85% of the time. Conclusions: The Gemini model achieved 78–85% accuracy in determining pancreatitis severity and adherence to treatment guidelines but showed lower accuracy in nutrition timing compared to other parameters. Core Tip: This study evaluated the Google Gemini model in applying the American College of Gastroenterology 2024 guidelines for acute pancreatitis across 512 Medical Information Mart for Intensive Care-III cases. Results demonstrated 85% accuracy in severity classification, precise prediction of Acute Physiology and Chronic Health Evaluation II and Ranson scores, and 78–85% compliance with nutritional and necrotizing pancreatitis management guidelines. These findings suggest that artificial intelligence-based clinical decision support systems can provide rapid, consistent, and guideline-concordant recommendations, which are particularly valuable in settings with limited specialist expertise.

Background: Differentiating the immunohistochemical and histopathological features of ductal carcinoma in situ (DCIS) lesions is critical to potentially enable future tailored treatment options and de-escalation strategies. Purpose: To investigate the potential of magnetic resonance imaging (MRI) radiomics for the non-invasive prediction of estrogen receptor (ER), progesterone receptor (PR), grade, microcalcification, and comedonecrosis in DCIS lesions, which may provide implications for risk stratification. Material and Methods: This single-center retrospective study included 75 DCIS patients who had undergone pre-treatment MRI and were diagnosed through surgical pathology at our hospital between January 2010 and December 2023. The 75 segmented lesions were categorized into subgroups based on ER, PR, comedonecrosis, microcalcification status, and grade. Model performance was then assessed. Manual segmentation was performed on the second phase of dynamic contrast-enhanced T1 MRI. The BORUTA method was employed for feature selection. Using the selected features, the classification performance of various models was examined to determine if DCIS could be differentiated across the five subgroups. Results: In total, 75 patients with DCIS (74 women; mean age=49.36±11.28 years) were evaluated. Effective discrimination in subgroups was as follows: comedonecrosis, n =5; grade, n =1; ER, n =3; and PR, n =5. Radiomic model performance varied across subgroups, with F1 scores observed in the subgroups: ER, 0.8293–0.9091; PR, 0.7796–1; grade, 0.9051–0.9538; microcalcification, 0.9126–0.9895; and comedonecrosis, 0.8247–1. Conclusion: MRI radiomics demonstrates significant potential in non-invasively differentiating key immunohistochemical and histopathological features of DCIS. This approach may provide complementary information for DCIS characterization and risk stratification, although further validation is required before clinical implementation.