Exo Inc. acquired Medo Dx Pte, an artificial intelligence (AI) developer, to make its point-of-care ultrasound imaging even easier to use. Medo brings two FDA-cleared artificial intelligence algorithms that draw on a library of millions of ultrasound images and health data to expand the settings for ultrasound and the reduce the expertise required for its use. Terms of the deal were not disclosed.
Cleerly Inc. sees a bright future ahead after boosting its fundraising to date nearly five-fold with a $192 million series C. The new infusion brought the total invested in the company to $248 million, a solid endorsement of a company that hopes to transform cardiology with precision-based diagnostics that move away from indirect indicators to accurate measurements.
Volpara Health Technologies Ltd. joined forces with Microsoft Corp. to accelerate the research and development of software that uses mammograms to identify potential cardiovascular issues.
Megarobo Technologies Ltd. raised $300 million in its series C round financing to develop intelligence and automation solutions for life science companies. The financing was led by Goldman Sachs Asset Management LP, Asia Investment Capital Ltd., and GGV Capital.
Artificial intelligence (AI) and machine learning (ML) present regulators and payers alike with some interesting dilemmas, but that statement can be applied to patent offices and inventors as well. In this fifth installment in a series on AI in radiology, we’ll examine the hazards of acquiring and sustaining intellectual property protection for these algorithms, a much more complicated and complex undertaking than many developers might appreciate.
A new large-scale study has found combining artificial intelligence (AI) and radiologists to analyze breast cancer screenings can lead to better patient outcomes, when compared with unaided radiologists and the use of AI alone. The study, published in The Lancet Digital Health, evaluated the performance of an AI decision referral system developed by Vara (MX Healthcare GmbH).
The U.S. FDA may be the most advanced regulatory agency when it comes to artificial intelligence (AI) and machine learning (ML), but developers of these products still have little in the way of FDA guidance to work with in many instances. Cassie Scherer of Dublin-based Medtronic plc, told attendees at this year’s Food and Drug Law Institute annual conference that they should have a product change control protocol ready to go despite the absence of FDA guidance on the subject, an effort that will increase time to market but pay eventually big dividends.
Ibex Medical Analytics Ltd. extended the reach of its artificial intelligence (AI) powered pathology system, adding gastric cancer to existing CE mark approvals in prostate and breast cancer.
Diabeloop SA has just closed a series C funding round, securing $73 million to ramp global expansion for its DBL1 integrated smart system for patients with type 1 diabetes. “This will allow us to boost commercial roll-out and continue pursuing our growth strategy into Europe, the U.S. and Asia,” Erik Huneker, CEO of Diabeloop, told BioWorld.
Developers of artificial intelligence (AI) and machine learning (ML) algorithms in medical radiology tend to think of regulatory approval as the primary hurdle to market, but there is also the question of how to pay for the use of these products. Public and private payers obviously hold the purse strings, but appealing to payers is still not always as straightforward proposition as some would like, reinforcing the notion that coverage and reimbursement still combine to serve as one of the highest hurdles to market for AI and ML.
