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Related Concept Videos

Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

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Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
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[Artificial intelligence in cardiac surgery].

M Cypko1, M Y Emmert1,2, V Falk1,2,3,4

  • 1Klinik für Herz‑, Thorax-, und Gefäßchirurgie, Deutsches Herzzentrum Berlin, Augustenburger Platz 1, 10785, Berlin, Deutschland.

Der Chirurg; Zeitschrift Fur Alle Gebiete Der Operativen Medizen
|March 8, 2020
PubMed
Summary
This summary is machine-generated.

Deep neural networks, a breakthrough in artificial intelligence (AI), are now gaining traction in medicine. This article explores AI-based medical devices and their applications in cardiac surgery.

Keywords:
Cardiovascular medicineMachine learningMedical productsNeural networksTranslation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Medicine

Background:

  • Deep neural networks (DNNs) emerged as a significant advancement in computer science and AI.
  • Initial medical applications of DNNs received limited attention until high-ranking publications appeared in 2017.
  • The potential of AI in clinical medicine and research became widely recognized following these publications.

Purpose of the Study:

  • To discuss fundamental principles for understanding AI-based medical devices.
  • To provide an overview of current AI-based solutions specifically for cardiac surgery.
  • To address the evolving landscape of AI in healthcare, including regulatory considerations and market entry.

Main Methods:

  • Review of foundational AI principles relevant to medical devices.
  • Survey of existing AI-based solutions in the field of cardiac surgery.
  • Analysis of the current market and regulatory trends for AI in medicine.

Main Results:

  • AI, particularly DNNs, has demonstrated significant potential in medical applications.
  • A growing number of startups and established companies are developing AI-based medical devices.
  • Regulatory frameworks for AI medical devices are beginning to emerge.

Conclusions:

  • AI is rapidly transforming clinical medicine and biomedical research.
  • AI-based medical devices are poised for significant growth and adoption in cardiac surgery.
  • Understanding AI principles and regulatory landscapes is crucial for future development and implementation.