We enjoy paying attention to advances in AI, even if they aren’t happening in a market we serve. This week we’re noodling on the benefits (and risks) of artificial intelligence, machine learning, and data science for healthcare. AI applications in healthcare, from patient-facing chatbots and telemedicine to deeper integration with medical systems and instruments, could have a deep impact on how we relate to our health and how much our treatments improve.

Our first article takes a look at some common objections and roadblocks, including the oft-cited “AI will take our jobs” and the more personal issues of data safety and privacy. It also takes a look at some of the benefits adding AI into healthcare will bring. Read “Arguing the Pros and Cons of Artificial Intelligence in Healthcare.”

CB Insights looks at AI + healthcare through the lens of venture capital and financial projections in “Top Healthcare AI Trends to Watch.” We agree that raising $4.3B (yep, billion) across 576 deals since 2013 is something to take note of.

 

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The benefits of AI for diagnoses and health disease progression probability predictions are clear. Story after story is hitting the news featuring AI diagnosing problems and reading charts and images with higher accuracy than humans in many cases. The latest of these stores is “AI predicts risk of death from heart disease more accurately than experts.” These stories run the risk of giving everyone a savior mentality when it comes to AI – we’ve got a ways to go before AI can drive itself when it comes to healthcare. But when it does reach a more autonomous state, won’t having AI to augment your doctor’s skills and save her some labor and time to diagnosis be fantastic?

Medical Device magazine has a breakdown of some of the important distinctions between automation and AI when it comes to healthcare in “Automation vs AI in medtech: where are we headed?”

We’re big on reducing waste and creating radical efficiency here at Noodle, so we appreciate the many ways AI can do both of those things for the healthcare industry. This CEO is speaking our language about predictive analytics and health data in “How AI in Health Care is Identifying Risks and Saving Money.”

Last, but not least, we’re intrigued by many ways scientists, startups, and doctors keep joining their efforts to further the usefulness and accuracy of AI for healthcare. The latest merging of the minds comes from the new AAIH, a joint effort between 15 startups and research teams. There is no such thing as “too many cooks” when it comes to figuring out how to make healthcare better.

Photo by Marcelo Leal on Unsplash

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