Caresyntax GmbH, a Berlin-based provider of surgical automation, analytics and AI software technologies, picked up $45.6 million in venture funding. The funds will be used to accelerate U.S. and global expansion and support continued development and deployment of Caresyntax products. Participating in the financing were Whiz Partners, a Takeda-back drug discovery gateway investment limited partnership, Plug and Play Tech Center, Barco Healthcare, Mitsubishi Corp., Relyens, IPF Partners and Caresyntax founders Dennis Kogan and Bjoern von Siemens.
HONG KONG – SK Holdings Co. Ltd. invested ₩10 billion (US$8.6 million) in Standigm Inc., an artificial intelligence (AI)-powered biotech company based in Seoul, Korea. It is the second big investment Standigm has attracted this year after a ₩13 billion series B round in March.
A neuropsychologist consult is typically the first step for a neurologist in aiding in the diagnosis and monitoring of neurological conditions. But timely appointments for an assessment by these specialists can be difficult to obtain, even under the best of circumstances. To better enable neurologists to assess which patients are most in need of a consultation with a neuropsychologist, Royal Philips NV has launched an artificial intelligence (AI)-based cognitive assessment tool in the U.S. Known as Philips Intellispace Cognition, the digital, cloud-based assessment tool takes established neuropsychological tests and enables their administration by a medical assistant via a tablet in an office setting.
HONG KONG – South Korean AI-based biotech Azoth Bio Inc., of Seongnam, Gyeonggi-do, and biopharmaceutical venture Wellmarker Bio Co. Ltd., based in Seoul, have signed a memorandum of understanding for cancer drug R&D and commercialization. Under the agreement, the two entities will use Azoth's AI-powered platform to develop Wellmarker's cancer treatment candidates.
Deciding which patients should go into the intensive care unit (ICU) after surgery is a difficult call and typically made entirely at the surgeon's discretion. The result is that surgeons typically err on the side of caution by putting more post-operative patients in the ICU than necessary. To aid in better ICU decision-making, physicians at New York University Langone Hospital System (NYU Langone) developed a machine learning algorithm that combs through a patient's electronic medical record to identify relevant factors to determine if they needed the ICU after surgery.
Radiologists review thousands of images a day. The hope is that artificial intelligence (AI) applications will become useful soon to verify diagnoses, prioritize queued images and even to offer a level of detection and measurement that aren't feasible for humans. One of the latest efforts on this front is by researchers at the University of California at San Francisco (UCSF) and the University of California at Berkeley.
CLEVELAND – What are some of the biggest challenges related using to artificial intelligence (AI) in health care? A panel of experts tackled that question during a session Tuesday during the 2019 Medical Innovations Summit, while also discussing what their organizations have done in this space to advance patient care.
Palo Alto, Calif.-based startup Doc.ai is training its sights on the $9.5 billion global epilepsy market, with the aim of using artificial intelligence to help patients find the best medication to control their seizures. To that end, the company is teaming up with the Stanford University School of Medicine and the Stanford Epilepsy Center on a digital health trial to develop a predictive treatment model that will identify the right treatment at the right time for individuals living with epilepsy.
Mayo Clinic has entered a 10-year partnership with Google "to expand on the more than 200 projects already incorporating artificial intelligence (AI) and machine learning," Mayo Chief Medical Information Officer Steve Peters told BioWorld MedTech. The Rochester, Minn.-based health care organization expects Google's expertise in data science and search technology will help the clinic improve treatment and outcomes by developing machine learning models.
The field of artificial intelligence (AI) in medical practice is in its infancy, but a group of medical societies has published a paper that proposes the development of a code of ethics for artificial intelligence (AI) in radiology. The paper underscores a number of concerns, including that some developers fail to fully appreciate the potential consequences of seemingly innocent slip-ups in the development and validation of that algorithm.