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Reports: Healthcare Artificial Intelligence Market CAGR of 47%-50%

Three separate market reports agree that the healthcare artificial intelligence (AI) market will increase at a compound annual growth rate (CAGR) of between 47 percent and 50 percent over the next few years.

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Source: Getty Images

By Fred Donovan

- Three separate market reports agree that the healthcare artificial intelligence (AI) market will increase at a compound annual growth rate (CAGR) of between 47 percent and 50 percent over the next few years.

The Markets and Markets report forecasts that the healthcare AI market will reach $36.1 billion by 2025, growing at a CAGR of 50.2 percent during that period. A report by IT Intelligence Markets predicts a market increase at a CAGR of more than 47 percent. And a report by Energias Market Research splits the different at a CAGR of 48.7 percent, with a market value of $20 billion by 2014.

The reports also agree on the vendor powerhouses in the healthcare AI market: AWS, Google, General Vision, GE Healthcare, IBM, Intel, Microsoft, Nvidia, Siemens Healthineers, and Philips.

Other vendors profiled in the three reports include Xilinx, Johnson and Johnson, Medtronic, Enlitic, Next IT, Welltok, Icarbonx, Recursion Pharmaceuticals, Stryker, Careskore, Zephyr Health, Oncora Medical, Sentrian, Bay Labs, Atomwise, Deep Genomics, Cloudmedx, Zebra Medical Vision, Imagia Cybernetics, Precision Health AI, and Cloud Pharmaceuticals.

In its report, Markets and Markets cites large and complex data sets, soaring healthcare costs, improving computing power, and declining cost of hardware as key factors driving growth in the healthcare AI market. At the same time, growth could be held back by medical practitioners’ reluctance to adopt AI technology, lack of skilled workers, ambiguous regulatory guidelines for medical software, and fear of AI's impact on healthcare employment and care.

“The fear of AI is not new. It’s there, and it’s real.”

IDx Technologies Founder and CEO Michael Abramoff told a November hearing of the Federal Trade Commission (FTC) that he faced fear in his effort to develop an AI product that detects diabetic retinopathy.

IDx’s autonomous diagnostic AI system can be used at the point of care with no human reviewer or oversight. This moves specialty diagnostics from academia to the primary care setting, expanding the number of patients who can be examined and lowering testing costs, he said.

In 2000, Abramoff first proposed the system, when he came up with an algorithm that could be used for diabetic retinopathy diagnosis, but he faced opposition from other healthcare professionals and regulators.

Abramoff’s colleagues gave him the nickname the “Retinator” because “he will destroy jobs and also he’s not being safe for patients. Now, they think differently,” he related.

“The fear of AI is not new. It’s there, and it’s real. So, we need to manage that,” he said.

Abramoff was able to raise $22 million for system development and finally get FDA approval last year, close to two decades after he first developed the idea.

“It took a long time to do this, but now essentially the rules are set for how you approve autonomous AI,” he said.

“Technology used in a lab does not directly transfer to what we do in healthcare. Patient safety is paramount. If we don’t do it right, there will be pushback and we will lose all of the advantages that AI can provide in healthcare for better quality, lower costs, and better accessibility,” he said.

Also at the FTC hearing was ONC Chief Scientist Teresa Zayas Caban. She said that ONC had seen a surge in AI clinical applications, such as a Google-developed tool to detect metastatic cancer in patients in which the cancer has not yet spread.

“These tools have the potential to improve care but may require adaptation for successful clinical use,” she said.

To be widely used in healthcare, Caban said that AI devices need to meet certain criteria:

  • Demonstrate algorithms’ technical soundness
  • Perform as well as current standards of clinical care
  • Test across many situations
  • Enhance patient outcomes, increase practicality of use, and/or reduce costs

ONC’s role is to make sure the data is interoperable to support the development of AI and understand the data infrastructure issues and what standards are needed, Caban concluded. 

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