Medical Device Integration with AI: Enhancing Diagnosis and Treatment in Healthcare

EasyChair Preprint no. 12161

Abstract

In the ever-evolving landscape of healthcare, the integration of medical devices with artificial intelligence (AI) has emerged as a promising avenue for enhancing diagnosis and treatment. This synergy capitalizes on the capabilities of AI to analyze vast amounts of patient data generated by medical devices, leading to more accurate and timely diagnoses, personalized treatment plans, and improved patient outcomes. By leveraging machine learning algorithms, medical devices can interpret complex physiological signals, imaging data, and clinical parameters, aiding healthcare professionals in decision-making processes. Furthermore, AI-driven medical devices facilitate remote monitoring and real-time interventions, enabling proactive healthcare management and reducing the burden on healthcare systems. However, challenges such as data privacy concerns, regulatory compliance, and interoperability issues need to be addressed to fully realize the potential of this integration. Despite these challenges, the convergence of medical devices and AI holds immense promise for revolutionizing healthcare delivery, transforming traditional models of diagnosis and treatment, and ultimately, improving patient care.

Keyphrases: Artificial Intelligence, data privacy, diagnosis, Healthcare, Interoperability, machine learning, medical device integration, Patient Outcomes, remote monitoring, treatment

@Booklet{EasyChair:12161,
  author = {Battle Hurry},
  title = {Medical Device Integration with AI: Enhancing Diagnosis and Treatment in Healthcare},
  howpublished = {EasyChair Preprint no. 12161},

  year = {EasyChair, 2024}}