Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model.

Introduction. An absolute burden of over 35 million new cancer cases is predicted by 2050. Although significant progress has been made in the field of oncology via the use of surgical removal, radiation treatment, chemotherapy, and the introduction of immunotherapy, the overall rates of survival and prognoses for cancer patients are still unsatisfactory. In the last decade, intermittent fasting (IF) has become increasingly popular for weight control and potential health benefits. Additionally, research has primarily focused on investigating the impact of IF on metabolism, mitochondrial function, stress responses, repair mechanisms, and autophagy. The aim of the study. To make a literature review about the role of intermittent fasting in starving cancer cells. Materials and methods. This narrative review involved a comprehensive search through databases such as PubMed and Google Scholar. Distinct keywords such as - ″intermittent fasting″, ″intermittent fasting regimens″, ″calorie restriction″, ″oncology″, ″chemotherapy″ and ″tumor microenvironment″ were used. Results. IF exhibits significant impacts on the immune system's ability to fight against tumors by strengthening the ability of hematopoietic stem cells to replenish themselves and enhance immunosuppression. Under an IF program, certain tissues and organs exhibit enhanced resilience to various stressors. Emerging research shows that IF has the potential to enhance the effectiveness and tolerance of anticancer medicines, regulate carcinogenic influences, reprogram clock genes' rhythmic expression in tumor environments, inhibit tumor growth by modifying natural processes like insulin signalling, heme oxygenase-1, prevent the proliferation of myeloid-derived suppressor cells and priming the tumor microenvironment to support drug delivery that targets tumors. Cancer treatment via IF notably shields normal cells while raising the efficacy of chemotherapy (CT) and reducing CT-induced inflammation via several immunological, biochemical, and molecular mechanisms. Conclusions. Regularly practising fasting for more than one day may confer notable health benefits by protecting healthy normal cells against the deleterious effects of chemotherapy and radiation. The synergistic therapeutic impact of intermittent fasting alongside chemotherapy on tumors suggests that it enhances the efficacy of chemotherapy while also notably reducing chemotherapy-induced inflammation. While intermittent fasting shows promise for certain cancers, such as breast cancer, its efficacy for other types remains uncertain, necessitating further research and personalized treatment plans.