Transforming Cardiovascular and Neurological Care with AI: A Paradigm Shift in Medicine

Authors

  • Michidmaa Arikhad Department of computer science, American National University, Louisville Kentucky, Email: arikhadmichidmaa@gmail.com Author
  • Muhammad Waqar Department of electrical engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Email: Mwaqar@Nuaa.Edu.Cn Author
  • Arbaz Haider Khan Department of Computer Science, University Of Engineering and Technology Lahore, Email: Arbazhaiderkhan15@gmail.com Author
  • Azizul Hakim Rafi AI-Researcher, Department of Master of Science in Information Technology, American National University, Salem, Virginia. Email: rafiazizul96@gmail.com Author

Keywords:

Artificial intelligence, cardiovascular care, neurological care, predictive analytics, wearable technology, personalized medicine

Abstract

Artificial intelligence (AI) is transforming the landscape of cardiovascular and neurological care, offering unprecedented advancements in diagnosis, treatment, and patient management. This paper explores the role of AI in revolutionizing these critical domains, emphasizing its impact on diagnostic accuracy, predictive analytics, and personalized medicine. AI models have demonstrated superior diagnostic performance in detecting cardiac arrhythmias and ischemic strokes, achieving sensitivity and specificity levels exceeding those of traditional methods. Machine learning (ML) algorithms have enhanced predictive capabilities, allowing for early detection of adverse cardiac events and the progression of neurological conditions such as Alzheimer’s and Parkinson’s diseases. The integration of AI with wearable devices has further advanced real-time monitoring, enabling continuous tracking of vital parameters and symptoms. These technologies empower patients and clinicians alike, fostering proactive disease management and reducing the burden on healthcare systems. For example, AI-driven wearable devices have achieved over 97% accuracy in detecting atrial fibrillation and monitoring motor fluctuations in Parkinson’s patients, illustrating their clinical utility. Despite its potential, AI in healthcare faces challenges, including data heterogeneity, algorithmic transparency, and ethical concerns related to bias and privacy. Addressing these limitations through interdisciplinary collaboration, robust data governance, and explainable AI frameworks is essential for widespread adoption. This paper concludes by highlighting the future prospects of AI in cardiovascular and neurological care, including its potential to integrate multimodal data, enhance clinical decision-making, and drive innovation in personalized medicine.

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Published

2024-12-15

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