From funding to publishing: How a Canadian surgeon’s Silicon Valley-backed AI platform could transform research

From grant application to publication, the clinical research journey is often long and complex. An artificial intelligence-powered platform founded by a Canadian surgeon who now teaches at Stanford University aims to make the journey easier, faster and—hopefully—with better results. Backed by US$10 million in seed funding—mostly from Silicon Valley investors—Medeloop is software-as-service technology designed to streamline early-stage health research and clinical trials by simplifying various stages of the process, from grant applications, collection and harmonization of data, identification of biomarkers and trends, and manuscript submission. “We’ve been working on this product for a while and now it’s fully built, it’s been tested, it works,” said Dr. Rene Caissie, CEO of Medeloop, which was developed out of Stanford University, where he got a business and management degree after building an electronic medical records business system that was subsequently acquired by Telus. “We are starting to onboard our first cohorts and we’re negotiating with a number of universities on the east coast.” Read: Which patients will crash? How AI is powering early-warning programs and other healthcare innovations Medeloop uses AI at every key stage of the research process. For example, users looking for funding can use the platform to search through the hundreds of available grants that match their research, and to submit multiple applications. AI is also used to onboard research participants. Through the platform participants can sign documents such as consent forms, respond to surveys, upload symptoms using voice or text, scan and log their food intake using computer vision and connect with other patients from the same cohort. Meanwhile, during this onboarding stage researchers can retrieve EMRs from more than 16,500 hospitals and clinics and connect with more than 220 wearable devices including Apple Watch and Fitbit. “We don’t use Internet knowledge,” said Dr. Caissie, who was also chief of oral and maxillofacial surgery at Université de Montréal during part of the period when he was building his EMR company, Medesync. “We use vetted medical knowledge—we have 64 billion entries of vetted medical information in the database to query against and we have every drug ever approved by the FDA, at the molecular level all the way up to clinical manifestations for diseases that they can treat.” Read: New app uses AI to help Calgary medical students practise interacting with patients In addition to having AI aggregate, harmonize and clean data from different sources—ensuring compliance with industry privacy and security standards—Medeloop also makes it easier for researchers to analyse their data and tease out findings, said Dr. Caissie, who Instead of using programming code such as Python, researchers can simply “talk” to Medeloop using natural language. “If you’re a family physician, for example, and you want to study a part of your patient population that’s presenting with certain symptoms you think might be related to COVID, then you just go into Medeloop and type in something like ‘I’d like to look into post-COVID symptoms’ and start probing from there,” said Dr. Caissie. “Or maybe you noticed there’s an uptick in headaches in your patient population and you want to study that. You can get Medeloop to pull from your EMR data and connect with your patients’ wearables, or ask them to report symptoms through the (Medeloop) app. “You could run this study over a certain period—let’s say 12 months—and it’s all automated for you. You can analyse data without having to hire a team of data analysts or statisticians, and you’ll be able to write and submit a paper using the latest technical, logical advancements in AI.” In addition to Stanford, researchers at other universities are set to use Medeloop, said Dr. Caissie. University of California San Francisco is collaborating with Medeloop on breast cancer research. In Canada, Medeloop is working with Montreal-based McGill University—where Dr. Caissie was a faculty lecturer in surgery—on a number of disease-focused research projects, including ones on mitochondrial disease and a rare disease called Ehlers-Danlos syndrome. “If you look at very sophisticated research centres right now, like Stanford and McGill, they use over 12 software applications to run their research studies,” said Dr. Caissie. “But now there’s one tool, everything is centralized and it’s made easier—we’ve just diminished the time for them to conduct their research by 10x, we’ve reduced costs substantially. That’s why we’ve been so successful.” Read: Toronto hospital network appoints chief AI scientist in bid to improve healthcare

From funding to publishing: How a Canadian surgeon’s Silicon Valley-backed AI platform could transform research

Register for free access or login.

Related Topics