Two blue-chip high-tech companies are going in opposite investment directions.

IBM Sells Watson Health While Oracle Invests in Medical Records Company Cerner

There were two huge announcements made this week and both were concerned with using AI in the medical field. And it should not surprise us when we say the announcements went in opposite directions. That is to say one company, IBM has decided to drop out of the medical data business and has sold its AI-based Watson Health to Francisco Partners.

The second company is an IBM rival. Reports say Oracle has invested in a $28.3 billion deal to pick up electronic medical records firm Cerner. An article in and one from provided the details of the two AI announcements.

Last week, an analyst said IBM was trying to get rid of all assets that “divert attention and capital, as well as carrying the risk of reputational damage,” reported.

Big software and tech giants continue to make a push into healthcare with megadeals like Microsoft’s $19.7 billion bid for Nuance and two private equity firms picking up Cerner competitor Athenahealth in a $17 billion deal.

As we mentioned two different directions.

What Happened to Watson Health

An AI-driven healthcare package or process has long been a programmer’s and doctors’ dream. While much of that dream has come true in so many medical fields, it is the collection and interpretation of the data by algorithms that weren’t looking for the correct medical patterns at times that helped push the sale of Watson Health AI.

Collecting and reporting accurate patient data is the problem. MedStar Health sees sloppy electronic health records practices harming doctors, nurses, and patients. The hospital system took initial steps to focus public attention on the issue in 2010, and the effort continues today. MedStar’s awareness campaign usurps the “EHR” acronym, turning it into “errors happen regularly” to make the mission clear.

Take, for example, diagnosing medical conditions based on eye images. In one patient the eye is healthy; in another, the eye shows signs of diabetic retinopathy. Images of both healthy and “sick” eyes are captured. When enough patient data is fed into the artificial intelligence system, the algorithm will learn to identify patients with the disease.

Andrew Beam, a professor at Harvard University with private sector experience in machine learning, presented a troubling scenario of what could go wrong without anybody even knowing it. Using the eye example above, let’s say as more patients are seen, more eye images are fed into the system which is now integrated into the clinical workflow as an automated process. So far so good. But let’s say images include treated patients with diabetic retinopathy. Those treated patients have a small scar from a laser incision. Now the algorithm is tricked into looking for small scars.

Since the AI being used is connected to the actual health of an individual, it’s clear why this medical data must be accurate and understood completely by any algorithm.

The amount of improvement using AI in medical processes has been proven unequivocally. And we here at have brought you multiple stories about AI providing more accurate X-ray and MRI examinations. Where AI is training surgeons or doing the actual surgery on a patient. Perhaps with a little tweaking, Watson Health can continue to be part of the overall health care system in the U.S and most likely the world’s as well.

“IBM remains committed to Watson, our broader AI business, and to the clients and partners we support in healthcare IT. Through this transaction, Francisco Partners acquires data and analytics assets that will benefit from the enhanced investment and expertise of a healthcare industry-focused portfolio,” Tom Rosamilia, senior vice president at IBM Software, said in a statement.


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