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Investments in AI-based healthcare have exploded In recent years. But even with the belt tightening in 2022, digital health startups using artificial intelligence (AI) have received a whopping $3 billion in funding. That has left a lot of room for start-up AI companies to make their mark in healthtech, biotech and medtech.
It is clear that even as health systems struggle to right infrastructure to support AI’s need for massive data lakes and to access high-value or silo data, the industry remains optimistic about artificial intelligence. A Dec 2021 questionnaire For example, from health insurer Optum found that nearly half of healthcare managers use AI, while about 85% say they have an AI strategy.
These are six startups that have had an outstanding year, disrupting a variety of healthcare areas, from drug discovery and operational efficiency to disease detection and cell biology research.
Atomwise: AI for drug discovery
In August, San Francisco-based Atomwise, which develops AI systems for drug discovery, signed a research collaboration with pharmaceutical leader Sanofi, potentially worth $1.2 billion. According to a press releasethe deal includes “deep learning for structure-based drug design, enabling the rapid, AI-powered search for Atomwise’s proprietary library of more than 3 trillion synthesizable compounds.”
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Drug discovery depends on a first step of “hit identification”, identifying the right molecules – hits – that bind to a target protein and alter its function. According to an August 2020 VentureBeat article, Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in less than two days, speeding up a process that would normally take months or years.
In 2022, Atomwise also strengthened its management team and expanded its executive team. It will need that power in a competitive space that includes Verge Genomics, Certara, Insilico Medicine, Recursion, and Benevolent AI.
ClosedLoop AI: digging into patient data
It’s been a significant year for Austin, Texas-based ClosedLoop AI, since it raised $34 million in August 2021. The company, which provides a data science platform that enables healthcare organizations to use AI to improve outcomes and reduce costs, was selected to participate in the AWS Healthcare Accelerator for Health Equity, and it won a 2022 Best in Class Award for artificial intelligence in healthcare.
Founded in 2017, the ClosedLoop platform provides turnkey AI models and automation workflows for healthcare applications and manual processes involving data science tasks, examining patient data at the individual level, and analyzing data points. Healthcare providers’ organizations have used ClosedLoop to make decisions about medical interventions and preventive measures for problems such as chronic kidney disease or heart failure.
Top ClosedLoop AI Competitors include heavyweights such as DataRobot and Dataiku, as well as Abacus Insights and Jvion.
Digital diagnostics: identifying eye disease
Based in Iowa in 2018 Digital diagnostics made headlines when it became the first autonomous AI system to be approved by the US Food and Drug Administration.
But 2022 was good for the company, whose AI diagnostic system, the IDx-DR, could be used to identify diabetic retinopathy — one of the leading causes of blindness in the U.S. and other developed countries — as well as other serious eye diseases, including macular eye disease. edema. In August, Digital Diagnostics announced it had raised $75 million, one of the largest healthcare funding rounds this year.
“We have a strong mission and purpose to get our technology to patients who really need to be tested, especially healthcare providers who may be experiencing burnout or burnout,” Seth Rainford, co-founder, president and COO of Digital Diagnostics, recently told VentureBeat.
Cleerly: AI for cardiac imaging
New York-based Cleerly has been on a mission to transform cardiac care since its founding in 2017. It has had a massive 2022, raising a new $192 million round in July for its AI-based approach to translate advanced imaging science into a new approach to identifying people at risk for heart attack.
According to a company press release, the research that developed into Cleerly’s technologies was conducted at the Dalio Institute for Cardiovascular Imaging at New York-Presbyterian Hospital and Weill Cornell Medicine, including large-scale clinical trials involving more than 50,000 patients. It included the most comprehensive research in coronary imaging to explore how imaging can be used to better understand heart disease and project patient outcomes.
A Study of February 2022 published in the Journal of American College of Cardiology, found that Cleerly’s AI platform is “as good or better than invasive angiography” – helping to catch heart disease early, before patients start showing symptoms.
Owkin: finding the right medicine for every patient
Talk about a great 2022: the medical AI unicorn Owkin insured of $80 million in June of pharmaceutical leader Bristol Myers Squibb as a partner of the two companies in drug trials.
Founded in 2016, the Franco-American New York-based startup has developed a federated learning-based technology to accelerate drug discovery and development, using health data typically stored in silos, such as from US and European hospitals. .
Last week, Owkin announced two first-ever AI diagnostics approved for use in Europe. The first can predict whether a breast cancer patient will relapse after treatment, while the other can identify a biomarker that allows potentially life-saving treatment for colorectal cancer patients.
Deepcell: AI for cell biological research
Founded in 2017, Deepcell of Menlo Park, CA emerged from Stanford University and raised new funds in March to use artificial intelligence to find new ways to understand biology.
In 2020, Maddison Masaeli, co-founder and CEO of Deepcell, told VentureBeat that Deepcell’s AI-powered approach “is able to distinguish between cell types with greater accuracy than traditional cell isolation techniques that rely on antibody staining or similar methods.”
And in February 2022 interviewMasaeli explained that the Deepcell platform relies on deep neural networks as the “ultimate cell classification” so that the model “continues to learn from the images it collects.” Currently, that includes about 1.5 billion images.
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