AI detects pancreatic cancer up to 3 years before diagnosis  Pancreatic cancer remains one of t

AI in predictive medicine can forecast disease risks by analyzing health data, enabling early intervention and personalized treatment for better outco

The rise of AI in predicting children's future health risks raises profound ethical questions around privacy, consent, and discrimination. Balancing early intervention benefits with potential psychological harm and societal impacts is more crucial than ever. #HumanInTheLoop #AIinHealthcare #EthicsInAI #PredictiveMedicine

Healthcare systems in Europe are undergoing a transformation due to high costs of care. Remote measurement technologies (RMTs) such as wearable sensors, mobile ...

Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with the burden of chronic neurodegenerative diseases having doubled over the last 2 decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence (AI) and bioinformatic methods is changing this landscape. This paper explores this transformative journey, emphasizing the critical role of AI in neurology, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, integrating biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. In addition, telemedicine and web-based interactive health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions such as multiple sclerosis, Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease, and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of IT and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this paper, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in AI will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric health care.

In a world where risk itself is treated, health becomes a moving target. We may soon live in a system where no one is fully “healthy,”

A Dell Med resident develops an AI tool to detect liver disease early, improving diagnosis, patient outcomes, and advancing medical technology.

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The research findings primarily focus on individuals with a sleep disorder but may have broader implications. A recent study suggests that a simple blood test could help predict the onset of dementia...