We are proud to announce that our latest research has been published in the European Medical Journal. <\/p>\n\n\n\n
Congratulations to Fredrick Mutisya and the Smartbiotic scientific team on this achievement.<\/p>\n\n\n\n
Here is a quick overview by Fredrick Mutisya on how to explore the use of AI against antimicrobial resistance.<\/p>\n\n\n\n
Not all missing data are actually “missing.” Sometimes they’re just waiting for a microbiologist to explain them.<\/p>\n\n\n\n
One of the biggest challenges in antimicrobial susceptibility (AST) datasets is that the gaps are rarely random. They’re often a consequence of laboratory testing practices and microbiological rules\u2014not simply incomplete data.<\/p>\n\n\n\n
In our latest work, we asked a simple question:<\/p>\n\n\n\n
Should AI guess first, or should domain knowledge lead the way?<\/p>\n\n\n\n
The answer surprised us less than it should have:<\/p>\n\n\n\n
The takeaway is one I’ll keep repeating:<\/p>\n\n\n\n
In healthcare AI, biology should constrain the algorithms\u2014not the other way around.<\/p>\n\n\n\n
As we build clinical AI systems, success won’t come from bigger models alone. It will come from combining expert knowledge, deterministic clinical rules, and machine learning where it adds the most value.<\/p>\n\n\n\n
Proud to see this work published and grateful to my co-authors and collaborators at Smartbiotic. <\/p>\n\n\n\n