Research

MHP-Net: A Revolutionary AI Model for Accurate Liver Tumor Segmentation for Diagnosis and Therapy

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the disease, but manual segmentation by radiologists is labor-intensive and often results in variations based on expertise.

AI Detects Hidden Heart Disease Using Existing Scans Stored in Patient Records

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels that place them at a greater risk for cardiovascular events.

Groundbreaking TACIT Algorithm Offers New Promise in Diagnosing, Treating Cancer

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment of cancer and in the prescription of medicines. As recently published in Nature Communications, Jinze Liu, Ph.D., and Kevin Byrd, D.D.S., Ph.D., created Threshold-based Assignment of Cell Types from Multiplexed Imaging Data (TACIT), which assigns cell identities based on cell-marker expression profiles.

The Many Ways that AI Enters Rheumatology

High-resolution computed tomography (HRCT) is the standard to diagnose and assess progression in interstitial lung disease (ILD), a key feature in systemic sclerosis (SSc). But AI-assisted interpretation has the potential to improve the quantification and characterisation of SSc-ILD, making it a powerful tool for monitoring.

Giving Doctors an AI-Powered Head Start on Skin Cancer

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously, developed by an international team of researchers led by Monash University.

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support medical practice, AI models must be capable of processing multimodal data and have reasoning and problem-solving capabilities that resemble those of humans.

Scientists Argue for More FDA Oversight of Healthcare AI Tools

An agile, transparent, and ethics-driven oversight system is needed for the U.S. Food and Drug Administration (FDA) to balance innovation with patient safety when it comes to artificial intelligence-driven medical technologies. That is the takeaway from a new report issued to the FDA, published in the open-access journal PLOS Medicine by Leo Celi of the Massachusetts Institute of Technology, and colleagues.

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