AI serves as a pivotal tool in the early diagnosis and prediction of gastric cancer, ushering in innovative solutions to enhance patient outcomes through advanced therapeutic techniques. Gastric cancer, a leading cause of cancer-related mortality worldwide, underscores the critical importance of early detection and personalized treatment strategies. AI-driven approaches harness ML algorithms, deep learning techniques, and big data analytics to analyze diverse datasets, identifying patterns, biomarkers, and risk factors associated with gastric cancer development and progression [118, 119]. For early diagnosis, AI algorithms scrutinize comprehensive datasets encompassing patient demographics, medical history, symptoms, labora­tory tests, imaging studies, and histopathological findings, facilitating timely detection of gastric cancer lesions. Notably, systems like EndoBRAIN, an AI-based diagnostic system developed by Olympus, employ deep learning algorithms to analyze endoscopic images, accurately detecting early-stage gastric cancer lesions by discerning subtle abnormalities such as mucosal changes and dysplastic lesions [120]. Furthermore, AI-driven risk prediction models integrate individual patient charac­teristics, genetic predisposition, environmental factors, and lifestyle habits to stratify patients based on their likelihood of developing gastric cancer. A notable example is an ML model developed to predict gastric cancer risk using demographic, clinical, and lifestyle factors, enabling targeted screening and preventive measures for high­risk individuals. In the realm of precision medicine, AI algorithms delve into molecular profiling data, genomic alterations, and tumor biomarkers to tailor treatment strategies, identifying optimal therapeutic options for gastric cancer patients. Platforms like IBM Watson for Genomics leverage AI to analyze genomic sequencing data, identifying actionable mutations and potential targeted therapy options, thus aiding oncologists in selecting personalized treatment regimens and optimizing patient outcomes [121]. Moreover, AI-driven decision support systems, exemplified by initiatives like CancerLinQ, aggregate real-world cancer data to provide oncologists with real-time insights and evidence-based recommendations, enhancing treatment decision-making and optimizing therapeutic interventions for patients with gastric cancer [122]. In essence, AI’s integration into the early diagnosis, prediction, and treatment optimization of gastric cancer holds immense promise in revolutionizing patient care and improving clinical outcomes in the fight against this formidable disease.

Liver fibrosis, characterized by progressive scarring of liver tissue, is a significant consequence of chronic liver diseases like hepatitis B and C, non-alcoholic fatty liver disease (NAFLD), and alcoholic liver disease. This condition poses a substantial global health burden, often leading to liver dysfunction, cirrhosis, and eventual liver failure if not managed effectively. Nanomedicine, which involves the application of nanotechnology in medicine, presents promising avenues for the diagnosis, treat­ment, and management of liver fibrosis. One key aspect is the targeted delivery of therapeutics using nanoparticles, engineered to reach hepatic stellate cells (HSCs), pivotal in liver fibrogenesis [123]. For instance, lipid-based nanoparticles such as liposomes or SLNs, loaded with anti-fibrotic agents like pirfenidone or nintedanib, can specifically target activated HSCs within the fibrotic liver. By delivering therapeutic payloads directly to the site of fibrosis, nanoparticle-based drug delivery systems enhance drug efficacy while minimizing off-target effects [124]. Additionally, nanoparticles can encapsulate nucleic acid-based therapeutics such as small interfering RNA (siRNA) or microRNA (miRNA) to selectively target and downregulate genes involved in fibrogenesis and inflammation. For example, polymeric nanoparticles conjugated with siRNA targeting transforming growth factor-beta (TGF-β) or platelet-derived growth factor (PDGF) receptors can inhibit signaling pathways implicated in HSC activation and collagen deposition [125]. Nanoparticles also serve diagnostic purposes by acting as contrast agents for non­invasive imaging modalities like MRI and PET, allowing accurate assessment of liver fibrosis severity and disease progression monitoring. Moreover, theranostic nanoparticles integrate diagnostic and therapeutic functions into a single platform, enabling simultaneous imaging and treatment of liver fibrosis. These innovations in nanomedicine hold promise for advancing the management of liver fibrosis, potentially leading to improved patient outcomes and enhanced quality of life.