A new medical large language model (LLM) achieved over 91 percent accuracy in identifying female participants diagnosed with major depressive disorder after analyzing a short WhatsApp audio recording where participants described their week, according to a study published in the open-access journal PLOS Mental Health by Victor H. O. Otani, from Santa Casa de São Paulo School of Medical Sciences and Infinity Doctors Inc., Brazil, and colleagues.
From a physiological perspective, heartbeats and breathing do not operate independently in the human body. Cardiac rhythm varies with the respiratory cycle, and the close interaction between the two is known as cardiopulmonary coupling (CPC). CPC reflects the regulatory state of the autonomic nervous system and serves as an important physiological indicator for evaluating sleep quality, cardiovascular health, and stress levels.
DiaCardia, a novel artificial intelligence model that can accurately identify individuals with prediabetes using either 12-lead or single-lead electrocardiogram (ECG) data, has been recently developed. This breakthrough holds promise for future home-based prediabetes screening using consumer wearable devices, without requiring invasive blood tests. This study emphasizes the utility of the ECG as a powerful biomarker and highlights that the innovative AI model can contribute to the prevention of diabetes.
A new artificial intelligence (AI) method called BioPathNet helps researchers systematically search large biological data networks for hidden connections - from gene functions and disease mechanisms to potential therapeutic approaches. BioPathNet was developed by teams at Helmholtz Munich and Mila - Quebec Artificial Intelligence Institute in Montreal, Canada. The researchers are now presenting the method in the journal Nature Biomedical Engineering.
A team of Mass General Brigham researchers has developed one of the first fully autonomous artificial intelligence (AI) systems capable of screening for cognitive impairment using routine clinical documentation. The system, which requires no human intervention or prompting after deployment, achieved 98% specificity in real-world validation testing. Results are published in npj Digital Medicine.
There are hundreds of cell types in the human body, each with a specific role spelled out in their DNA. In theory, all it takes for cells to behave in desired ways - for example, getting them to produce a therapeutic molecule or assemble into a tissue graft - is the right DNA sequence. The problem is figuring out what DNA sequence codes for which behavior.
Researchers at Colorado State University have determined how to use artificial intelligence (AI) to modify antibodies so they act as lightbulbs, enabling scientists to better see inside living cells to track errors in gene expression that can lead to cancer and other disorders.
The findings, published in Science Advances, outline an approach that is significantly faster than existing manual testing and development methods to address an ongoing challenge to see activity in tiny cells continuously and clearly.