Validating healthcare AI in your patient population

Healthcare AI Validation

Artificial intelligence (AI) and machine learning (ML) are quickly becoming must-have technologies in the healthcare space. With the growing deployment of AI algorithms in the clinical setting, it’s important for health systems to understand how an algorithm will perform within their workflow and most importantly on their patient population. 

How are AI algorithms validated in the healthcare setting? This is a question we’re asked frequently. The on-demand webinar Validation & Deployment Guidelines: Machine Learning in Healthcare helps answer this question by addressing…

  • Why validation is an important foundational step in deploying AI algorithms
  • Highlighting the unique challenges of machine learning vs traditional software 
  • Deployment workflows and how they impact AI algorithm evaluation
  • What are thresholds in AI validation and why are they important 
  • How to overcome machine learning deployment challenges with ModelOps
  • Guideline examples for evaluation commercial AI solutions 

Watch the on-demand webinar, Validation & Deployment Guidelines: Machine Learning in Healthcare.

 

Do you have questions regarding the performance of AI algorithms in use at your hospital? 

Interested in learning more about validating and deploying AI at your hospital? 

Leave a comment or drop us a note, we’re happy to share our experience, connect you with other AI users or send you a copy of the ECLAIR Guidelines: Evaluating Commercial AI Solutions in Radiology. 

Picture of Kathleen Poulos

Kathleen Poulos

Kathleen is a registered nurse with a digital marketing background, a love for using technology to solve healthcare challenges and a passion for improving patient care.

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CASE STUDY

ARA Health Specialists

Use the button below to download your free case study and learn how our approach to validation has improved the number of clinically significant findings in AI software.

CASE STUDY

Sutter Health

Use the button below to download your free case study and learn how our approach to validation has improved the number of clinically significant findings in AI software.