AI is employed in healthcare for various applications, including medical image analysis, disease diagnosis, personalized treatment planning, and patient monitoring. It utilizes machine learning, natural language processing, and data analytics to improve diagnostic accuracy, optimize treatment outcomes, and enhance healthcare delivery, leading to more efficient and effective patient care.
NYU Tandon researchers have developed an AI-powered food recognition system that accurately estimates calories and macronutrients from meal photos, eliminating the need for manual food tracking.
Scientists have developed an AI-powered Intelligent Acting Digital Twin (IADT) that can autonomously control real-world machines in real-time, marking a shift from passive monitoring to active decision-making.
AI has the potential to revolutionize health care by streamlining workflows, enhancing diagnostics, and improving patient outcomes, but barriers such as data privacy, high costs, and regulatory challenges slow its adoption. This comprehensive review offers a roadmap to overcome these hurdles and unlock AI's full potential in medicine.
A new paper in Engineering explores how artificial intelligence can advance beyond large language models (LLMs) by focusing on knowledge empowerment, model collaboration, and model co-evolution. These strategies aim to tackle LLM limitations like hallucinations, inefficiency, and poor interpretability.
AI language models, like ChatGPT, exhibit heightened anxiety responses when exposed to traumatic content, mirroring human emotional biases. Researchers found that therapeutic prompts can help stabilize AI behavior, offering a cost-effective alternative to retraining.
Researchers from KIIT and Chandragupt Institute of Management explore how machine learning transforms big data challenges into opportunities, enabling industries to harness vast data resources effectively.
AI is transforming diabetes care by predicting risks, personalizing treatments, and improving disease management, making healthcare more accurate, cost-effective, and accessible.
Artificial intelligence may help predict schizophrenia and bipolar disorder years before diagnosis, allowing earlier intervention and treatment. Researchers from Aarhus University used machine learning on electronic health records to identify high-risk patients.
A new study reveals that while GPT-4 excels at analogical reasoning in standard tests, it struggles with variations, highlighting its reliance on pattern-matching rather than true understanding.
Researchers developed an AI system that improves tracking of urban green spaces using satellite imagery, significantly enhancing accuracy and revealing disparities in vegetation distribution.
A global study of 10,000 participants across 20 countries reveals that fear of AI replacing human workers varies significantly by culture, with judges and doctors being the most concerning AI-driven roles. The research highlights that fear is driven by a mismatch between AI capabilities and the psychological traits required for these jobs.
AI can now predict disease outbreaks before they happen, helping governments and healthcare systems act faster. With cutting-edge models, scientists are revolutionizing pandemic response, saving lives, and shaping the future of global health.
AI systems in high-stakes industries often fail to provide clear explanations for their decisions, leaving users vulnerable. Researchers from the University of Surrey propose the SAGE framework to ensure AI-generated explanations are transparent, user-friendly, and ethically sound.
A new study finds that people trust AI for low-stakes decisions like music recommendations but are skeptical in high-stakes areas like healthcare—especially those with strong statistical literacy.
A national survey found that 65.8% of adults distrust their healthcare systems to use AI responsibly, highlighting concerns about safety and transparency in AI adoption.
Researchers developed a smart AI-powered heating jacket with color-changing yarns to prevent overheating, enhancing safety for users, particularly the elderly.
edars-Sinai is testing the Aiva Nurse Assistant, an AI-powered mobile app that allows nurses to document patient information through voice dictation, reducing administrative workload and improving efficiency. The pilot aims to enhance patient care by freeing up nurses’ time for meaningful interactions.
Researchers found that people distrust artificial moral advisors (AMAs), particularly when they offer utilitarian advice, even if the advice is identical to that of human advisors.
As a network of websites with a truly global audience, AZoNetwork is joining the global effort to close the gender gap. Since the first AZoNetwork website, AZoM, was launched in 2000, we have seen increased representation and recognition, with more women winning Nobel Prizes.
Researchers from NJIT, Rutgers, and Temple University are developing AI security education programs to address adversarial machine learning threats, aiming to equip future engineers with robust defense strategies.
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