Zhonghua Jie He He Hu Xi Za Zhi. 2026 Jan 12;49(1):78-83. doi: 10.3760/cma.j.cn112147-20250728-00445.

ABSTRACT

Rare lung diseases are a group of diseases characterized by significant clinical heterogeneity, challenging diagnosis and treatment processes, and diverse underlying causes. Due to their uncommon symptoms and limited awareness among healthcare providers, these diseases are frequently misdiagnosed or diagnosed too late, resulting in poor patient outcomes and placing a significant healthcare burden on the healthcare system. However, in recent years, the rapid advancements in artificial intelligence (AI) technology within the medical field have created new opportunities for early identification, accurate diagnosis, and personalized management of these diseases. A variety of AI techniques, ranging from traditional machine learning to more recent methods such as deep learning, reinforcement learning, and transfer learning, have been employed in areas such as clinical decision support, radiomics, omics data analysis, and the prediction of treatment responses for rare lung diseases. This article systematically reviews the latest research progress of AI applications in idiopathic pulmonary fibrosis, cystic fibrosis, idiopathic pulmonary arterial hypertension, and other rare lung diseases. It also emphasizes AI's potential benefits in disease classification, treatment evaluation, and prognosis prediction through illustrative research examples.

PMID:41483922 | DOI:10.3760/cma.j.cn112147-20250728-00445

World J Gastroenterol. 2025 Dec 14;31(46):111176. doi: 10.3748/wjg.v31.i46.111176.

ABSTRACT

Artificial intelligence (AI) is rapidly transforming the landscape of hepatology by enabling automated data interpretation, early disease detection, and individualized treatment strategies. Chronic liver diseases, including non-alcoholic fatty liver disease, cirrhosis, and hepatocellular carcinoma, often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operator-dependent imaging. This review explores the integration of AI across key domains such as big data analytics, deep learning-based image analysis, histopathological interpretation, biomarker discovery, and clinical prediction modeling. AI algorithms have demonstrated high accuracy in liver fibrosis staging, hepatocellular carcinoma detection, and non-alcoholic fatty liver disease risk stratification, while also enhancing survival prediction and treatment response assessment. For instance, convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis (F2-F4) and 0.89 for advanced fibrosis, with magnetic resonance imaging-based models reporting comparable performance. Advanced methodologies such as federated learning preserve patient privacy during cross-center model training, and explainable AI techniques promote transparency and clinician trust. Despite these advancements, clinical adoption remains limited by challenges including data heterogeneity, algorithmic bias, regulatory uncertainty, and lack of real-time integration into electronic health records. Looking forward, the convergence of multi-omics, imaging, and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care. Continued efforts in model standardization, ethical oversight, and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.

PMID:41479639 | PMC:PMC12754151 | DOI:10.3748/wjg.v31.i46.111176

Gut Liver. 2025 Dec 31. doi: 10.5009/gnl250268. Online ahead of print.

ABSTRACT

The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semiannual ultrasonography and the measurement of α-fetoprotein levels, offer suboptimal sensitivity for early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies. In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.

PMID:41472345 | DOI:10.5009/gnl250268

Medicina (Kaunas). 2025 Dec 11;61(12):2196. doi: 10.3390/medicina61122196.

ABSTRACT

Background and Objectives: Although beaded filament structural protein 1 (BFSP1) may be involved in oncogenic mechanisms, its clinical relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. This study examined the prognostic significance, regulatory mechanisms, and potential therapeutic implications of BFSP1 in LIHC. Materials and Methods: Comprehensive bioinformatics analysis was performed across multiple platforms using datasets derived from The Cancer Genome Atlas. Differential gene expression, DNA methylation, copy number variation, immune cell infiltration, drug sensitivity, and co-expression networks were systematically examined. Functional enrichment analyses of protein-protein and gene-gene interaction networks were conducted using STRING and GeneMANIA. Additionally, short interfering RNA-mediated knockdown and wound-healing assays were performed in HepG2 cells to evaluate BFSP1 function in vitro. Results: The results showed that BFSP1 mRNA expression was significantly upregulated in tissues from LIHC patients. Elevated BFSP1 levels were associated with poorer prognostic patterns, which were further supported by detailed clinicopathological subgroup analyses. Furthermore, BFSP1 expression was correlated with promoter hypomethylation and associated with patterns of tumor-infiltrating immune cells, including specific immune cell subtypes such as M1 and M2 macrophages. Integrative analyses revealed strong associations between BFSP1 and drug sensitivity, as well as a regulatory network encompassing genes involved in the cell cycle, DNA repair, and metabolic processes. Functional knockdown of BFSP1 significantly reduced HepG2 cell migration in vitro, as assessed by wound healing assay, with decreased wound closure at 24 h (11.0% vs. 16.5%) and 48 h (7.4% vs. 12.5%) compared with the control (p < 0.05, n = 6 biological replicates). Conclusions: In conclusion, these findings suggest that BFSP1 functions as a multifaceted prognostic biomarker and a potential therapeutic target for LIHC.

PMID:41470198 | PMC:PMC12735119 | DOI:10.3390/medicina61122196