Why Healthcare ML Models Often Fail at Deployment

Common reasons include:

• Poor workflow integration
• Lack of interpretability
• Misalignment with clinical needs
• Limited trust from users

Addressing these factors is key to successful deployment.

My work focuses on applying ML with these considerations in mind.

I am open to remote roles globally.

Follow my work here:

Medium
https://medium.com/@fora12.12am

Substack
https://substack.com/@glazizzo

Dev.to
https://dev.to/onyedikachi_onwurah_00ba3

Feedcoyote
https://feedcoyote.com/onyedikachi-ikenna-onwurah

Facebook
https://www.facebook.com/profile.php?id=61587376550475

https://www.facebook.com/groups/1710744006974826/

https://www.facebook.com/groups/1583586269613573/

https://www.facebook.com/groups/787949350529238/

LinkedIn
www.linkedin.com/in/onyedikachi-ikenna-onwurah-0a8523162