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Challenges for augmenting intelligence in cardiac imaging.

Partho P Sengupta1, Damini Dey2, Rhodri H Davies3

  • 1Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.

The Lancet. Digital Health
|August 30, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in cardiac imaging offers automation but lacks proven cost benefits and requires more clinical trials. The focus should shift to AI augmenting human judgment for better patient care.

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Area of Science:

  • Cardiovascular Imaging
  • Medical Artificial Intelligence
  • Health Informatics

Background:

  • Deep learning-based Artificial Intelligence (AI) has advanced automation and prediction in cardiac imaging.
  • Despite significant investment, AI has not yet demonstrated tangible healthcare cost reductions or improved patient outcomes.
  • Methodological development and clinical trials are insufficient to establish AI's superiority over human interpretation.

Purpose of the Study:

  • To review key studies on AI in cardiac imaging.
  • To identify challenges hindering AI implementation and effectiveness.
  • To propose a pragmatic shift towards augmented intelligence in cardiac imaging.

Main Methods:

  • Review of existing studies on AI applications in cardiac imaging.
  • Analysis of challenges including data scarcity, privacy, ethical concerns, and model biases.
  • Discussion of integration hurdles with Picture Archiving and Communication Systems (PACS) and data sharing limitations.

Main Results:

  • AI training is impeded by data scarcity, privacy, and ethical issues.
  • Lack of unified cardiac models and evolving knowledge introduce biases.
  • Integration into clinical workflows is challenging due to data quality and standardization issues.

Conclusions:

  • AI in cardiac imaging should be viewed as augmented intelligence, complementing human judgment, not replacing it.
  • The focus should shift to integrating complex data for phenotype identification and pattern recognition.
  • Enhancing imaging reports with AI can improve patient understanding and shared decision-making, necessitating professional standards and guidelines.