A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within tumours, opening doors for more targeted therapies for patients.
Findings on the development and use of the AI tool, called AAnet, have today been published in Cancer Discovery, a journal of the American Association for Cancer Research.
Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways to efficiently support medical diagnoses. Yet these systems also entail considerable risks - for example, they can "hallucinate" and generate false information.
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.
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.
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.
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.
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.