Virtual cells in intelligent oncology
October 2025
Computational pathology: A comprehensive review of recent developments in digital and intelligent pathology
April 2025
Computational pathology, a field at the intersection of computer science and pathology, leverages digital technology to enhance diagnostic accuracy and efficiency. With the digitization of pathology...
Virtual staining for pathology: Challenges, limitations and perspectives
April 2025
In pathological examinations, tissue must first be stained to meet specific diagnostic requirements, a meticulous process demanding significant time and expertise from specialists. With advancements...
Medical multimodal large language models: A systematic review
October 2025
The rapid advancement of artificial intelligence (AI) has ushered in a new era of medical multimodal large language models (MLLMs), which integrate diverse data modalities such as text, imaging, physiological...
Regulatory sandbox expansion: Exploring the leap from fintech to medical artificial intelligence
April 2025
This paper explores the expansion from fintech-based regulatory sandboxes to those that include medical artificial intelligence (AI) by examining their potential to foster innovation and accelerate...
Artificial intelligence in surgical oncology: A comprehensive review from preoperative planning to postoperative care
October 2025
While artificial intelligence (AI) has demonstrated significant potential across medical fields, its surgical applications, particularly in oncology remain largely exploratory. This review synthesizes...
Multimodal medical imaging AI for breast cancer diagnosis: A comprehensive review
January 2026
Traditional artificial intelligence (AI)-based methods for breast cancer diagnosis often rely on a single modality, such as ultrasound images. With the rise of multimodal approaches, multiple data sources,...
AI dermatology: Reviewing the frontiers of skin cancer detection technologies
April 2025
The rapid advancements in artificial intelligence (AI) have significantly impacted modern healthcare, particularly for skin cancer detection in the field of dermatology. Skin cancer has become a considerable...
Integrating multi-omic liquid biopsies and artificial intelligence: The next frontier in early cancer detection
January 2026
The integration of multi-omic liquid biopsies with artificial intelligence (AI) represents a rapidly evolving frontier in early cancer detection, offering the potential to enhance personalized medicine...
UD-TN: A comprehensive ultrasound dataset for benign and malignant thyroid nodule classification
April 2025
The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging. However, publicly available thyroid ultrasound datasets remain scarce. In this study,...
Deep learning-based segmentation of small-volume brain metastases in lung cancer patients
April 2026
Brain metastases from lung cancer typically present as multiple small lesions, creating considerable challenges for accurate segmentation. While existing datasets and models have primarily focused on...
Predicting the effectiveness of neoadjuvant therapy in rectal cancer patients: Model construction based on radiomics and carcinoembryonic antigens
January 2026
This study aimed to develop a multimodal imaging histological model based on computed tomography (CT) images and carcinoembryonic antigen (CEA) values to predict the efficacy of preoperative neoadjuvant...
Integrative multi-omics clustering for identifying novel breast cancer subtypes with distinct molecular and clinical characteristics
January 2026
As a heterogeneous disease, breast cancer requires refined classification frameworks that can effectively guide targeted therapies. However, traditional methods fail to capture the comprehensive molecular...
Artificial intelligence in tumor drug resistance: Mechanisms and treatment prospects
April 2025
Artificial intelligence (AI) demonstrates unprecedented potential in the study of tumor drug resistance and precision therapy. With the rapid growth of multi-omics data and biomedical information, AI...
A narrative review of the prediction of immunotherapy efficacy for treating NSCLC: An artificial intelligence perspective
July 2025
Immunotherapy efficacy in non-small cell lung cancer (NSCLC) remains variable, with traditional biomarkers (programmed death-ligand 1 [PD-L1] and tumor mutational burden) limited by heterogeneity and...
Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support
July 2025
Bone tumors (BTs)—including osteosarcoma, Ewing sarcoma, and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location, histological...
Assessing quantitative performance and expert review of multiple deep learning-based frameworks for computed tomography-based abdominal organ auto-segmentation
April 2025
Segmentation of abdominal organs in computed tomography (CT) images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up. However, manually generated...
Decision-making performance of large language models vs. human physicians in challenging lung cancer cases: A real-world case-based study
January 2026
Despite the promise shown by large language models (LLMs) for standardized tasks, their multidimensional performance in real-world oncology decision-making remains unevaluated. This study aims to introduce...
Current status and prospects of artificial intelligence in liver cancer management
July 2025
Liver cancer is an extremely heterogeneous malignant tumor characterized by high morbidity and mortality rates. Despite significant advancements in cancer care, the outcomes of liver cancer patients...
AI-based diagnosis of clear-cell renal cell carcinoma based on non-contrast CT
April 2026
The accurate characterization of renal tumors, particularly clear-cell renal cell carcinoma (ccRCC), traditionally requires contrast-enhanced computed tomography (CECT), which is contraindicated in...
A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities
July 2025
This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis, specifically targeting automated quantitative...
Harnessing computational power for intelligent oncology in the age of large models: Status, challenges, and prospects
January 2026
The integration of large-scale foundation models (e.g., GPT series and AlphaFold) into oncology is fundamentally transforming both research methodologies and clinical practices, driven by unprecedented...
Advancing precision oncology through hNQO1-activatable NIR-II probes: Integrating molecular imaging with artificial intelligence
April 2026
Traditional imaging modalities often lack the molecular specificity and spatial resolution required for real-time tumor visualization, particularly in complex surgical settings. This narrative review...
Deep learning in abdominal organ segmentation: A review
October 2025
Abdominal organ segmentation is an essential and fundamental medical procedure with many clinical and research applications. There is extensive variability in the size, location, and shape of the abdominal...