This week’s Domain Knowledge highlights the process involved in deploying artificial intelligence (AI) in the healthcare space. For this post, we’re focused on the area of continuous performance monitoring after an algorithm has been launched at a hospital. We look at why monitoring is needed, as well as setting standards and accountability for AI utilization.

3 steps toward setting and sustaining standards for medical AI details three pillars as a rationale for why standardization of testing and performance measures is critical to the success of AI. They go on to say, the responsibility for this standardization falls on physicians and medical societies.

Wondering if readers agree? Should responsibility also extend to the AI vendors? Drop a comment below and let us know what you think.

The Harvard Business Review states that it’s not easy to know how to responsibility manage and deploy AI systems. How to build accountability into your AI walks the reader through governance, data, performance, and monitoring – offering a framework to manage and evaluate AI in the real world.

While imaging AI gets a lot of press, there are limited resources addressing how to incorporate AI into the daily workflow. 6 steps for seamlessly integrating an artificial intelligence solution into daily clinical practice offers functional steps for using AI to add value to the care process. The authors share potential limitations and sample use cases.

Radiology AI requires monitoring after deployment, an article from leadership at the American College of Radiology (ACR), stresses the point that performance needs to be monitored on an ongoing basis. ACR suggests an AI data registry could provide a way to monitor declines in AI performance and enable data from multiple institutions to be aggregated for peer learning and best practice development.

That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an end-to-end AI platform to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog. 

If you have AI tips, suggestions, or resources you’d like to share leave us a note below and please feel free to suggest topics that you would like to see covered in future posts.