Difference between revisions of "Challenges in AI Implementation in Healthcare"
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| + | == Introduction == | ||
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| + | Currently, there are several challenges in artificial intelligence (AI) implementation in the healthcare setting, ranging from ethical to practical. Despite the wealth of promising new AI technologies, few have seen successful implementation into clinical workflows. The disparity between the promising performance statistics and the lack of ultimate clinical efficacy has been deemed the “AI chasm.” Several healthcare organizations have developed AI governance committees to help evaluate potential AI tools and ease difficulties in implementation [1]. This wiki entry is meant to be a broad but high-level overview of various challenges facing AI translation to clinical care. | ||
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| + | == Technical Challenges == | ||
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| + | == Research Challenges == | ||
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| + | == Organizational Challenges == | ||
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| + | == End User Challenges == | ||
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| + | == References == | ||
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Submitted by Isabella Slaby, DO | Submitted by Isabella Slaby, DO | ||
[[Category:BMI512-SPRING-24]] | [[Category:BMI512-SPRING-24]] | ||
Revision as of 15:08, 3 May 2024
Contents
Introduction
Currently, there are several challenges in artificial intelligence (AI) implementation in the healthcare setting, ranging from ethical to practical. Despite the wealth of promising new AI technologies, few have seen successful implementation into clinical workflows. The disparity between the promising performance statistics and the lack of ultimate clinical efficacy has been deemed the “AI chasm.” Several healthcare organizations have developed AI governance committees to help evaluate potential AI tools and ease difficulties in implementation [1]. This wiki entry is meant to be a broad but high-level overview of various challenges facing AI translation to clinical care.
Technical Challenges
Research Challenges
Organizational Challenges
End User Challenges
References
Submitted by Isabella Slaby, DO
