How AI is Solving Today’s Problems with Samir Manjure of KenSci
While we often think about machine learning and artificial intelligence as a way to replace humans, it might not be so simple in the healthcare space. We need those humans who have spent decades mastering this craft, as their intuition and empathy cannot be matched by machines. At the same time, we have to…
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While we often think about machine learning and artificial intelligence as a way to replace humans, it might not be so simple in the healthcare space. We need those humans who have spent decades mastering this craft, as their intuition and empathy cannot be matched by machines. At the same time, we have to recognize that there are some machine learning and data analyzation functions that could save doctors and healthcare staff a lot of valuable time.
Enter assistive intelligence.
Today KenSci CEO Samir Manjure joins the MarketScale podcast to speak about assistive intelligence in the healthcare space. Manjure started his career at Microsoft and was working his way up through the machine learning and data sciences ladders, but wanted to do something that would have a meaningful impact on society at large. His top choices were education and healthcare, then he found his future KenSci co-founder with whom he realized that the healthcare market was underserved. So, they decided to start KenSci and “use data science in a way that can benefit the society at large.”
Although, Manjure is quick to point out that the company is not using data science for large scale applications, like finding the cure for cancer. KenSci is very applied and is using machine learning to “solve today’s problems.”
The question has changed from why we should use AI to how, and in the healthcare industry that is in a variety of ways, including pharmaceutical variation, determining the length of a patient’s stay, predicting emergency room overflow, determining whether a patient is pre-diabetic, and even predicting congestive heart failure or which patients are likely to leave without being seen by a doctor.
At the end of the day, says Manjure, “It’s not about the sexy machine learning, it’s about saving lives and making an impact in the patient’s care and procedures.”
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