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Episode Synopsis
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Summary
This 2019 paper reviews the applications of artificial intelligence (AI) in healthcare, focusing on the last five years of research. The authors examine AI's use with various data types—multi-omics, clinical (including medical images and electronic health records), behavioral, environmental, and pharmaceutical research and development data—highlighting current successes and challenges. Key challenges discussed include data integration, balancing model interpretability with performance, ensuring model security, addressing data bias, and enabling federated learning. The paper concludes by emphasizing the need for integrative analyses, greater model transparency, robust security measures, and strategies to mitigate data bias to fully realize AI's potential in healthcare.
Summary
This 2019 paper reviews the applications of artificial intelligence (AI) in healthcare, focusing on the last five years of research. The authors examine AI's use with various data types—multi-omics, clinical (including medical images and electronic health records), behavioral, environmental, and pharmaceutical research and development data—highlighting current successes and challenges. Key challenges discussed include data integration, balancing model interpretability with performance, ensuring model security, addressing data bias, and enabling federated learning. The paper concludes by emphasizing the need for integrative analyses, greater model transparency, robust security measures, and strategies to mitigate data bias to fully realize AI's potential in healthcare.
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