Matthijs Luxen et al. comment on Neda Vishlaghi et al.: https://doi.org/10.1172/JCI201199
Address correspondence to: Benjamin Levi, Department of Surgery, University of Texas Southwestern, 6,000 Harry Hines Blvd., Dallas, Texas 75,235, USA. Phone: 214.648.9017; Email: Benjamin. [email protected].
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1Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
An AI-powered model developed at the University of Michigan can read a brain MRI and diagnose a person in seconds, a study suggests. The model detected neurological conditions with up to 97.5% accuracy and predicted how urgently a patient required treatment.
Researchers say the first-of-its-kind technology could transform neuroimaging at health systems across the United States. The results are published in Nature Biomedical Engineering.
“As the global demand for MRI rises and places significant strain on our physicians and health systems, our AI model has the potential to reduce burden by improving diagnosis and treatment with fast, accurate information,” said senior author Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.
Google has collaborated with African universities and research institutions to launch WAXAL, an open-source speech database designed to support the development of voice-based artificial intelligence for African languages.
African institutions, including Makerere University in Uganda, the University of Ghana, Digital Umuganda in Rwanda, and the African Institute for Mathematical Sciences (AIMS), participated in the data collection for this initiative. The dataset provides foundational data for 21 Sub-Saharan African languages, including Hausa, Luganda, Yoruba, and Acholi.
WAXAL is designed to support the development of speech recognition systems, voice assistants, text-to-speech tools, and other voice-enabled applications across sectors such as education, healthcare, agriculture, and public services.
Retinal vein occlusion (RVO) is a severe disease that occurs when a vein in the light-sensitive layer at the back of the eye (i.e., the retina) becomes blocked, which results in a loss of vision. There are currently a few medical interventions that address RVO, including the periodic injection of medications that block the abnormal growth of blood vessels or of steroids, which reduce swelling and inflammation.
A promising procedure for the treatment of RVO is retinal vein cannulation (RVC). This is a very delicate surgical intervention that requires surgeons to insert a tiny needle into the blocked vein with high precision, delivering clot-dissolving drugs or medications that control the abnormal growth of blood vessels.
Given that retinal veins targeted for cannulation are similar in thickness to a human hair, manually inserting a needle inside them with high precision is very challenging. Robots could potentially assist surgeons in performing RVO procedures, ensuring that needles are inserted correctly and without damaging the patients’ retina.
Mass General Brigham investigators have developed a robust new artificial intelligence (AI) foundation model that is capable of analyzing brain MRI datasets to perform numerous medical tasks, including identifying brain age, predicting dementia risk, detecting brain tumor mutations and predicting brain cancer survival. The tool, known as BrainIAC, outperformed other, more task-specific AI models and was especially efficient when limited training data were available.
Results are published in Nature Neuroscience.
“BrainIAC has the potential to accelerate biomarker discovery, enhance diagnostic tools and speed the adoption of AI in clinical practice,” said corresponding author Benjamin Kann, MD, of the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham. “Integrating BrainIAC into imaging protocols could help clinicians better personalize and improve patient care.”
Among people injecting drugs and not engaged in medical care, nearly all tested positive for fentanyl and multiple other substances, with polysubstance and xylazine detection rates highest among unhoused and recently incarcerated participants.
This cross-sectional study used data from HPTN 094. Participants who met eligibility criteria were invited to participate in a baseline interview and were enrolled between June 2021 and September 2023. All participants completed written informed consent prior to participating in study procedures, and a single institutional review board (Advarra) provided ethical approval for HPTN 094; this cross-sectional analysis was exempt from additional IRB approval. The current study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.23
Participants were required to meet the following criteria: be at least 18 years of age, have a urine test positive for recent opioid use and evidence of recent injection drug use (visible venipuncture marks), meet diagnostic criteria for opioid use disorder, be able to give informed consent, be willing to start MOUD treatment, complete an assessment of understanding, have confirmed HIV seropositivity or self-reported sharing of injection equipment and/or condomless sex in the past 3 months with partners living with HIV or with unknown HIV status, and provide locator information. Participants were excluded if they self-reported being prescribed MOUD in the 30 days prior to screening, had a urine test positive for methadone (with the exception of verified hospitalization), or were enrolled in another study.
Unraveling TIME: CD8+ T cell-and CXCL11-driven endocrine resistance in BreastCancer:
Tim Kong & Cynthia X. Ma provide a Commentary on Fabiana Napolitano: https://doi.org/10.1172/JCI188458
1Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.
2Department of Medicine, Weill Cornell Medicine, New York, New York, USA.
Address correspondence to: Cynthia X. Ma, Division of Oncology, Department of Medicine, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri, 63,110, USA. Email: [email protected].
For decades, researchers have attempted to pinpoint the specific areas of the brain responsible for human intelligence. A new analysis suggests that general intelligence involves the coordination of the entire brain rather than the superior function of any single region. By mapping the connections within the human brain, or connectome, scientists found that distinct patterns of global communication predict cognitive ability.
The research indicates that intelligent thought relies on a system-wide architecture optimized for efficiency and flexibility. These findings were published in the journal Nature Communications.
General intelligence represents the capacity to reason, learn, and solve problems across a variety of different contexts. In the past, theories often attributed this capacity to specific networks, such as the areas in the frontal and parietal lobes involved in attention and working memory. While these regions are involved in cognitive tasks, newer perspectives suggest they are part of a larger story.
Severe bleeding is one of the most common and preventable causes of death after traumatic injury, yet currently available tools have poor ability to determine which patients urgently need blood transfusions. A new multinational study, just published in Lancet Digital Health, suggests artificial intelligence (AI) may help close that gap.
Researchers have developed and validated machine-learning models that can accurately predict whether trauma patients will require blood transfusions, using only information available before they reach the hospital such as vital signs, injury patterns, and medication history.
Co-author Prof Patricia Maguire from University College Dublin (UCD), Director of UCD AI Healthcare Hub and UCD Institute for Discovery, said, “These findings show that AI-driven decision support could enable earlier and more precise identification of patients at highest risk of hemorrhagic shock, using data already available to emergency services. This has clear potential to support more timely transfusion decisions, although prospective evaluation will be needed before clinical implementation.”