Front Digit Health. 2026 Sep 16;8:1851723. doi: 10.3389/fdgth.2026.1851723. eCollection 2026.

ABSTRACT

Artificial intelligence (AI) is being applied across diagnostic and therapeutic workflows in hepatology. This narrative review summarizes recent advances in AI for liver disease. In medical imaging and digital pathology, computer vision enables automated quantitative analysis of ultrasound, CT, MRI, and histologic images, with the aim of improving the consistency of lesion detection, disease staging, and prognostic assessment. In biomarker research, machine learning can analyze high-dimensional liquid-biopsy and multi-omics data to develop diagnostic and prognostic models; some have outperformed conventional markers in their study cohorts. Electronic health records (EHRs) and large language models (LLMs) are also being investigated for clinical decision support and personalized management. However, most reported evidence remains retrospective, and clinical adoption is limited by data heterogeneity, poor interpretability, uncertain generalizability, and regulatory requirements. Progress will require standardized datasets, external and prospective validation, clinically relevant endpoints, and human-centered implementation before gains in model performance can be translated into better patient outcomes.

PMID:42819088 | PMC:PMC13624904 | DOI:10.3389/fdgth.2026.1851723

J Inflamm Res. 2026 Mar 17;19:581764. doi: 10.2147/JIR.S581764. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have significantly improved the clinical outcomes for patients with non-small cell lung cancer (NSCLC). However, patient heterogeneity and the limitations of current biomarkers contribute to variations in therapeutic responses. Identifying potential beneficiaries of immunotherapy and predicting efficacy remain critical challenges. In recent years, artificial intelligence (AI) has become increasingly applied in cancer treatment, particularly for modeling clinical data and predicting patient prognosis. By integrating multi-omics data such as radiomics, pathomics, genomics, transcriptomics, proteomics, and microbiomics, AI enables comprehensive biomarker discovery and facilitates prediction of immunotherapy responses and potential toxicities in NSCLC patients. Despite these advancements, challenges such as data standardization, limited interpretability, and technical barriers persist. This review summarizes the application of AI in predicting immunotherapy efficacy for NSCLC patients and discusses the challenges and future directions in the context of precision medicine.

PMID:41867453 | PMC:PMC13005593 | DOI:10.2147/JIR.S581764