AI Medical Record Scanning

Artificial Intelligence (AI) systems continue to pervade many industries, usually where there is a lot of data and human developers struggle to build solutions that handle the complexities of the problem. Often the experts in these subject areas can’t fully articulate the reasoning behind how and why they use data to arrive at some conclusion. We developers often struggle to get clear specifications from clients in simple cases, so I certainly understand why AI might be attractive in complex problem spaces.

I think AI is a promising way to try to tackle some of these issues, some of which are important to humans. These systems can achieve a focus and analysis of complex data in a way that very few people, if any, can. The sheer volume of data and myriad of relationships among the various different metrics captured eludes the ability of most people to properly analyze.

I found an article that looks a neat non-intrusive way of analyzing medical records from patients to detect blood issues. In this case, an AI looks at the records of treatment and test results, looking behind the doctors and nurses to catch patterns that can indicate blood poisoning. The signs are subtle, and in today’s world where the humans are often overloaded, hand-offs between people can be incomplete. This means that doctors and nurses sometimes miss things. The AI doesn’t order treatment or prescribe anything, but raises a flag to alert humans. The medical staff can then review things, examine the patient, and decide on the treatment.

Early results seem promising, and more importantly, this is the type of lever that computing can bring to leverage human expertise and help humans. Not only can they better treat patients, but potentially this can help enhance the understanding of what signs a doctor or nurse should look for in future patients. When a flag is raised and an issue detected, the staff can go over notes or discuss how they might have caught this themselves. A little continuous learning applied between humans and machines, just what we’d want in a DevOps software pipeline in technology.

Steve Jones

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