Background: The COVID-19 pandemic affected many people worldwide, including those with chronic diseases. Our objective was to analyze its influence on medical care and the course of inflammatory bowel disease (IBD) in Poland. Material/Methods: In 2021, 81 patients in Poland with IBD completed an original anonymous questionnaire about the impact of the COVID-19 pandemic on the course of their disease and mental status. The printed questionnaire was distributed to IBD patients treated at the Gastroenterology Outpatient Clinic of the University Clinical Hospital in Bialystok, and an online questionnaire was sent to patients via social media. Statistical analysis was performed using the chi-squared test, with a significance level of P<0.05. Results: The study group consisted of 46 women and 35 men with a mean age of 32.42 years. Fifty-nine patients had ulcerative colitis and 22 had Crohn disease. Patients reported significant deterioration in medication availability (50.62%) and restricted access to gastroenterology outpatient clinics (51.90%) (P<0.05). Of patients who contracted COVID-19, 89.47% did not require hospitalization, 32.10% (26/81) were asymptomatic, mild, or moderate, despite immunosuppressive biological treatment (27.16%, 22/81), or steroids (18.52%, 15/81). Over 50% of respondents stated the pandemic negatively affected their mental state and 30% of them associated that with worsening IBD. Conclusions: During the pandemic, respondents were mainly concerned with difficulties in accessing the gastroenterology clinic and limited drug availability. The pandemic negatively affected patients' mental state. In cases of COVID-19 disease, patients with IBD were mostly asymptomatic and did not require hospitalization, despite therapy affecting the immune system.

Acute pancreatitis (AP) is one of the most common gastrointestinal diseases and is characterized by a highly variable clinical course ranging from mild self-limiting inflammation to severe disease associated with persistent organ failure and high mortality. Early risk stratification is essential for timely therapeutic decision-making and improved patient outcomes. Although conventional prognostic scoring systems remain widely used, their predictive performance is limited by moderate accuracy, delayed applicability and the requirement for numerous clinical or imaging parameters. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of predictive models capable of integrating demographic, clinical, laboratory and imaging data to improve early outcome prediction. This review summarizes current evidence on the application of AI and ML in AP, focusing on the prediction of disease severity, organ failure, intensive care unit admission and mortality. We also discuss explainable AI approaches and the methodological strengths and limitations of existing models, including validation, calibration, overfitting and reproducibility. Finally, we highlight future perspectives, including multimodal AI, prospective clinical validation and regulatory considerations that should facilitate the safe integration of AI-based decision-support tools into routine clinical practice.