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...
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...
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...
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,...
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...
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,...
Artificial intelligence in neuro-oncology imaging: Advancing brain tumor detection, grading, and treatment response evaluation
July 2026
This narrative review synthesizes how artificial intelligence (AI) is reshaping neuro-oncology imaging through automated detection, segmentation, grading, and longitudinal monitoring of brain tumors...
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...
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...
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...
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...
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...
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...
Monte Carlo–consistent dose prediction for clinical CyberKnife radiotherapy using a physics- and spatially-informed diffusion model
July 2026
Although Monte Carlo (MC) dose calculation is the gold standard for CyberKnife radiotherapy, its clinical integration is hindered by prohibitive computational latency arising from stochastic particle...
TSMIL: Transformer-based structured low-rank end-to-end multi-instance learning network for renal cell carcinoma classification in whole-slide images
April 2026
The pathological classification of renal cell carcinoma (RCC) is a critical indicator of its accurate diagnosis, treatment, and prognosis. Pathologists typically focus on a single subtype when determining...
The role of artificial intelligence in cancer epidemiology: Challenges and opportunities
July 2026
Artificial intelligence (AI) is rapidly reshaping cancer research, but high technical performance alone is not sufficient for cancer epidemiology, which requires population representativeness, measurement...
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...
From cells to patients: Multiscale computational pathology in the era of foundation models and vision-language systems
July 2026
Computational pathology is fundamentally defined by its inherent hierarchical structure, spanning from nuclear morphology and cellular interactions to tissue microenvironments, ultimately integrating...
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...
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...
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...
Integrative machine learning-driven prioritization of ceRNA networks in adrenocortical carcinoma
April 2026
Adrenocortical carcinoma (ACC) is a rare and highly aggressive endocrine malignancy in urgent need of robust biomarkers and novel therapeutic targets. In this study, a machine learning (ML)-driven framework...
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...