What can AI healthcare startups learn from the recent IBM Watson-Anderson fallout?

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Four years ago, global cancer-care pioneers MD Anderson announced a collaboration with IBM Watson to power ‘Moon Shots’ – a mission aimed at ending cancer by using the former’s cognitive computing system. The ambitious system, IBM Watson-powered Oncology Expert Advisor, would integrate MD Anderson’s knowledge of clinicians and researchers to advance the goal of treating patients with the most effective, evidence-based, and safe standard of care.

However, a few months ago, Anderson announced that it was putting the project on hold. 

Watson Health’s broad offerings

With a mission to “improve lives and enable hope”, IBM is attempting to solve diverse healthcare challenges to improve diagnosis outcomes as well as engage in medical research. Their vast product portfolio includes precision medicine in genomics, ‘Watson for Drug Discovery’, health patient engagement, oncology and care management and automated diagnosis in care delivery and research.

Has it created any real difference on the ground?

It’s tough to ascertain.

With investments to the tune of US$4 billion already, Watson Health is an extremely comprehensive project in terms of application offerings and integration in the existing systems. However, Watson Health’s complexity has so far only acted as a bottleneck and not an enabler in adoption or proving any kind of noteworthy utility. One plausible reason: the entire project is much ahead of its time.

Yet, one can’t deny that the healthcare sector in emerging and developed markets has tremendous opportunity to optimize care delivery, outcomes, and costs using cognitive computing. And keeping this in mind, IBM’s bet on this sizeable market makes sense.

So does that mean cognitive computing/AI in healthcare has failed?

But does the fallout of the Anderson deal and the complex nature of Watson’s system necessarily indicate failure of cognitive computing/AI in Healthcare? Or simply put, is the need of the hour different?

Increment-focused niche solutions are need of the hour.

With its broad-based end-to-end solutions, covering a plethora of diseases and aiming to replace doctors, Watson Health could be a play in the distant future. But today, increment-focused niche solutions are the need of the hour. Intelligent engines independently focusing on discrete problem spaces that exist within the healthcare delivery value chain would be more impactful in the near future – with them successfully solving problems bottom-up and acting as a fundamental baseline in creating a broader, connected offering.

Watson’s most prominent offering is providing a second opinion to a physician’s diagnosis on a multitude of diseases by aggregating data from zillions of clinical journals and correlating it with patients’ data and suggesting the best alternative treatments. All this comes at an additional cost and does not, by any means, replace the need of a physical doctor. The adaptability, thus, has been difficult.

On the other hand, smaller, innovative startups are showing better signs of adaptability through focused solutions.

• Israeli startup Medymatch is building deep and advanced cognitive analytics in stroke diagnosis emergencies. Medically, when a stroke patient arrives at the hospital, doctors have an extremely small window to make a right diagnosis, often resulting in 30 percent misdiagnoses. Medymatch’s offerings reduce the 20-minutes reading time to just 2 minutes, significantly increasing the efficiency and accuracy of diagnoses.
• Another startup, US-based Arterys – a SaaS analytics platform to revolutionize medical imaging – visualizes and precisely quantifies blow flow support to cardiovascular disease diagnostics using MRI and cloud computation, which currently is an extremely tedious task as segmentation and measurement need to be performed manually, leading to no reports at all.

Medymatch and Arterys establish effectively that different clinical streams have a wide variety of problem spaces and highly-targeted solutions not only provide better value proposition but also ease of integration and adaptability by the end-users i.e., consumers.

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Imminent white spaces

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