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2/10/2026

Inaugural GAFIO General Assembly Charts Vision for Global AI-Driven Cancer Care, Marking the Alliance’s Official Launch

February 9, 2026 (BOSTON, MA) – The Global Alliance for Intelligent Oncology (GAFIO), an international non-profit academic organization, convened its Inaugural General Assembly...

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2/5/2026

Important Reminder: GAFIO 2026 General Assembly Participation Guide

Dear Colleagues and Partners, The GAFIO 2026 General Assembly will be held online on February 9, 2026, at 8:00 PM EST (Boston, MA, USA). To ensure your smooth participation, please make the following preparations in advance:

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1/27/2026

GAFIO 2026 Annual Global Assembly | Join Us Online

The Global Alliance for Intelligent Oncology (GAFIO) will host its Annual Global Assembly in February 2026. We hereby extend a formal invitation to all members, partners, and experts worldwide. This assembly will focus on "Collaboration and Innovation in Intelligent Oncology," aiming to unite international expertise and shape the future of the field.

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Medscape Medical News Interpretable Multimodal Model Enhances Prediction of Chemotherapy Response in Muscle-Invasive Bladder Cancer

A new study published in npj Digital Medicine highlights the potential of an interpretable deep learning framework to improve the prediction of responses to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC). The Graph-based Multimodal Late Fusion (GMLF) model, integrates histopathological images and gene expression data to predict treatment outcomes and identify key biomarkers.

Interpretable Multimodal Model Enhances Prediction of Chemotherapy Response in Muscle-Invasive Bladder Cancer

A new study published in npj Digital Medicine highlights the potential of an interpretable deep learning framework to improve the prediction of responses to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC).

AI-based ovarian cancer ultrasound image analysis model performs well in international verification

Stockholm, Sweden – In a groundbreaking study, researchers from the Karolinska Institute, led by Professor Elisabeth Epstein, have developed an AI-driven ultrasound image analysis model that demonstrates exceptional performance in detecting ovarian cancer. Published in Nature Medicine...

Enhancing Full-Field Mammogram Classification with the Multi-Scale Region Selection Network (MRSN)

Recently, the journal Medical Image Analysis has accepted an article titled "Multi-scale region selection network in deep features for full-field mammogram classification,"

MOSA Deep Learning Model: Synthesizing Cancer Multi-Omic Data for Precision Oncology

A pioneering study published in Nature Communications introduces MOSA (Multi-Omic Synthetic Augmentation), an unsupervised deep learning model designed to integrate and augment multi-omic datasets of cancer cell lines, significantly enhancing the understanding of cancer biology and drug response mechanisms.

Application of Deep Learning Models in Colon Tumor Detection: Enhancing Diagnostic Accuracy of NET and CRC

Recently, a groundbreaking study titled "Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor" has been published in Cell Reports Medicine

Revealing the Proteome-wide Effects of Disease Mutations with a Deep Learning Framework

Currently, there are many challenges in translating genetic and genomic discoveries into precision medicine. To address these challenges...

Intelligent Oncology Successfully Launched

Intelligent Oncology, associated with the Global Alliance for Intelligent Oncology has been successfully launched and is now accepting submissions.

Enhancing AI Pipeline Reproducibility in Radiology with Cloud Infrastructure

As artificial intelligence (AI) continues to make inroads in the medical field, its application in radiology has become crucial for improving patient care and enhancing diagnostic accuracy. However, the issues of transparency and reproducibility in AI research have long plagued the development of the field. Recently, a study published in "Nature Communications" demonstrated how cloud-based infrastructure can implement and share transparent and reproducible AI pipelines in radiology, providing new momentum for clinical translation.

Deep Learning for Breast Cancer Risk Prediction: A Groundbreaking Study on Senescence Markers in Healthy Tissue

Recently, an article titled " Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study" was published in The Lancet Digital Health.

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