• Explainable, Domain-Adaptive, and Federated AI for Clinical Applications - a conversation

  • 2024/11/24
  • 再生時間： 24 分
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Explainable, Domain-Adaptive, and Federated AI for Clinical Applications - a conversation

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  • Enjoy this paper as a host/guest podcast to make the complex simple

    Summary

    This research review explores three key methodological approaches to enhance the use of artificial intelligence (AI) in medical decision-making. Explainable AI focuses on making AI models more transparent and interpretable to build trust. Domain adaptation addresses the challenge of applying AI models trained on one dataset to different datasets. Federated learning enables the training of large-scale AI models without compromising patient data privacy by using distributed collaboration. The paper provides an overview of existing methods, examines their applications in medicine, and discusses challenges and future directions. The authors also analyze current research trends in each area, highlighting strengths and limitations.
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Enjoy this paper as a host/guest podcast to make the complex simple

Summary

This research review explores three key methodological approaches to enhance the use of artificial intelligence (AI) in medical decision-making. Explainable AI focuses on making AI models more transparent and interpretable to build trust. Domain adaptation addresses the challenge of applying AI models trained on one dataset to different datasets. Federated learning enables the training of large-scale AI models without compromising patient data privacy by using distributed collaboration. The paper provides an overview of existing methods, examines their applications in medicine, and discusses challenges and future directions. The authors also analyze current research trends in each area, highlighting strengths and limitations.
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