AI-Driven Multimodal Frameworks for Cardiovascular Diagnostics: Integrating Sensors, Imaging, and Robotic Systems

Cardiovascular disease is still the leading cause of death, and a definitive cure has not yet been found, so this is the time to make important changes in prevention and early diagnosis. Integrating artificial intelligence, machine learning, wearable sensors, and biomedical imaging is changing healthcare for cardiovascular care. Recent breakthroughs have reported that the use of smart immune sensors and artificial intelligence helps diagnose disease by testing blood and urine samples. The upcoming advances from various emerging technologies are expected to greatly enhance the overall accuracy and personalization of diagnostic processes within the medical field. This essay explores difficulties relating to domain adaptation, variability in data, and interpretability, including the need for rigorous validation tests and ethical considerations. The new system is made up of several programs that help the user to make decisions more efficiently in situations where rapid action is needed, while considering privacy preservation, clinical quality improvement, and energy efficiency. This review of more than sixty recent studies is an attempt to broaden the field of cardiovascular care by introducing a roadmap for further research. The creation of a fully responsive cardiovascular diagnostic system is not yet complete and requires the contribution of several entirely different fields of science.

@article{T2025,
  title     = {AI-Driven Multimodal Frameworks for Cardiovascular Diagnostics: Integrating Sensors, Imaging, and Robotic Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Chandrasekhara Reddy T and Ramesh Babu P},
  doi       = {10.14569/IJACSA.2025.0161146},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161146}
}