With IBM‘s recent exploration to sell off its health business unit IBM Watson Health, The Wall Street Journal highlighted several issues with AI in healthcare that can hinder tech companies’ innovation efforts.
Despite spending several billion dollars on acquisitions to scale Watson Health, IBM‘s health business currently isn’t profitable and is looking to sell, according to the Journal. IBM declined to comment on the sale, but offered the following statement to the publication about its successes over the past decade.
“This work began nearly 10 years ago, at the beginning of the AI revolution, and we explored groundbreaking space in helping physicians advance healthcare through AI,” the company said. “IBM is continuing to evolve the Watson Health business, based on our decade of experience, to meet the needs of patients and physicians.”
Here are four things to know about the challenges AI tech faces in healthcare, according to the report.
Four things to know:
1. Attempting to apply AI to treating complex medical conditions is one of the main challenges the industry faces, with contributing factors such as human, financial and technological barriers as well as getting access to data that represents patient populations broadly, healthcare experts told the Journal.
2. Tech companies sometimes also lack the deep expertise and knowledge of how healthcare works, which can exacerbate struggles of implementing AI in patient settings, said Thomas Fuchs, dean of AI and human health at New York City-based Mount Sinai Health System.
“You truly have to understand the clinical workflow in the trenches,” he said. “You have to understand where you can insert AI and where it can be helpful.”
3. Some tech industry AI projects such as IBM Watson Health may be overly ambitious about healthcare’s growth market; they sometimes take on too broad, but complicated health questions. IBM Watson Health, for example, was marketed broadly at finding answers to all types of cancer, the healthcare experts said.
4. The lack of data collection standards can also contribute to AI challenges in healthcare because it makes taking an algorithm developed in one specific setting difficult to apply in other situations.
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