Challenges in AI Implementation in Healthcare

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.

Research Challenges

Many are calling for more rigorous standards to be applied to AI research. The majority of research for algorithms is retrospective, looking at historically-labeled data to train and test algorithms [8]. More prospective studies are needed to understand the performance of AI as a clinical intervention or CDS with real-time patient data. More peer-reviewed randomized control trials are also needed to assess predictive or diagnostic AI algorithms regarding clinical effectiveness and whether there is any difference in patient outcomes. Controlled clinical trials would also be needed to compare different algorithms developed for the same purpose to compare performance. [2,9]

References

Submitted by Isabella Slaby, DO