Seoul’s sleep data and AI research company HoneyNaps, founded in 2015, has now raised over US$16 million to help solidify its stature in the United States medical tech market.
A hefty Series B funding round that pulled in about three-quarters of that sum closed this week.
“These resources will enable us to achieve results in the domestic and American medical market and earn recognition,” the company’s chief financial officer said in a news release.
The startup has developed an FDA-approved software that can accurately diagnose sleep disorders with the help of machine learning. It has also created a platform called HoneyCube, which monitors body functions throughout the night and works in tandem with the company’s “SOMNUM” software.
There are currently more than 100 million people throughout the world who suffer from disorders like insomnia and sleep apnea. HoneyNaps aims to help mend that problem. The vast majority of them do not receive adequate treatment.
HoneyNaps intends to use its software program to help diagnose other conditions in the future too.
“Beyond the SOMNUM’s current use in sleep disease diagnosis, we plan to further advance the AI to expand its application to other critical areas such as cardiovascular disease, dementia and Parkinson’s disease,” the company said.
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New research examines benefits and flaws of AI in sleep medicine
A new paper assessed the potential advantages and disadvantages of the technology in this particular niche of medicine. That report was published in the Journal of Clinical Sleep Medicine on Monday.
The researchers concluded that AI contributes to the promotion of good sleep health. Several smart wearable devices, developed by companies like Hapbee Technologies, Inc. (TSX-V: HAPB) and others, have made it easier for people to get improved rest.
The three “pivotal areas” in sleep medicine where AI can be beneficial are clinical care, population health management and lifestyle management, the researchers say.
However, there are still significant concerns in this realm where healthcare and emerging technologies intersect.
“These include the need for generalizability of AI models across diverse populations, addressing resource limitations in implementing AI-enabled technology, safeguarding patient privacy in data collection and analysis, establishing clear guidelines for AI’s clinical use and ensuring that AI enhances rather than replaces human interaction in health care delivery,” the authors said.
rowan@mugglehead.com
