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A systematic review of federated learning applications for biomedical data

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Author summary Interest in machine learning as applied to challenges in medicine has seen an exponential rise over the past decade. A key issue in developing machine learning models is the availability of sufficient high-quality data. Another related issue is a requirement to validate a locally trained model on data from external sources. However, sharing sensitive biomedical and clinical data across different hospitals and research teams can be challenging due to concerns with data privacy and data stewardship. These issues have led to innovative new approaches for collaboratively training machine learning models without sharing raw data. One such method, termed ‘federated learning,’ enables investigators from different institutions to combine efforts by training a model locally on their own data, and sharing the parameters of the model with others to generate a central model. Here, we systematically review reports of successful deployments of federated learning applied to research problems involving biomedical data. We found that federated learning links research teams around the world and has been applied to modelling in such as oncology and radiology. Based on the trends we observed in the studies reviewed in our paper, we observe there are opportunities to expand and improve this innovative approach so global teams can continue to produce and validate high quality machine learning models.


Sony's new headphones boast Endel's generative soundscapes – TechCrunch

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The other day, Brian reported on Sony's new LinkBuds headphones, including its partnership with "what if Brian Eno was a piece of computer software" app Endel. The company uses really fascinating AI technology to generate soundscapes and music tracks to help your brain do its best work -- to help you focus deeper, sleep more easily or to relax you. I spoke with one of Endel's founders to learn more about the tech and its deal with Sony. "Endel is first and foremost a technology that was built to help you focus, relax and sleep. And the way this technology works, it procedurally generates a soundscape in real time on the spot, on the device. It is personalized to you based on a number of inputs that we collect about you; things like the time of day, your heart rate, the weather, your movement and your circadian rhythms, like how much sleep you got last night," explains Oleg Stavitsky, CEO and co-founder at Endel.


Artificial intelligence makes a splash in small-molecule drug discovery

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In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.


Can artificial intelligence overcome the challenges of the health care system?

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Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills. The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations. Once virtual, the AI Cures Conference returned to in-person attendance at MIT's Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB). MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the "transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise."


6 business risks of shortchanging AI ethics and governance

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Depending on which Terminator movies you watch, the evil artificial intelligence Skynet has either already taken over humanity or is about to do so. But it's not just science fiction writers who are worried about the dangers of uncontrolled AI. In a 2019 survey by Emerj, an AI research and advisory company, 14% of AI researchers said that AI was an "existential threat" to humanity. Even if the AI apocalypse doesn't come to pass, shortchanging AI ethics poses big risks to society -- and to the enterprises that deploy those AI systems. Central to these risks are factors inherent to the technology -- for example, how a particular AI system arrives at a given conclusion, known as its "explainability" -- and those endemic to an enterprise's use of AI, including reliance on biased data sets or deploying AI without adequate governance in place.


The role of AI and machine learning in revolutionizing clinical research - MedCity News

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Advanced technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have become a cornerstone of successful modern clinical trials, integrated into many of the technologies enabling the transformation of clinical development. The health and life sciences industry's dramatic leap forward into the digital age in recent years has been a game-changer with innovations and scientific breakthroughs that are improving patient outcomes and population health. Consequently, embracing digital transformation is no longer an option but an industry standard. Let's explore what that truly means for clinical development. Over the years, technology has equipped clinical leaders to successfully reduce costs while accelerating stages of research and development.


How AI is transforming remote cardiac care for patients - MedCity News

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The pandemic accelerated the advancement of artificial intelligence (AI) in remote patient care. Physicians are increasingly using digital patient monitoring to better track health data, identify abnormalities, and provide patient-specific treatment -- all without the need for in-person interaction. Additionally, emergency departments are employing remote monitoring solutions to allow some patients to leave the hospital sooner. These transformative technologies are leading to better outcomes for patients and reduced healthcare costs. AI use cases continue to grow in healthcare, as constant learning and training of algorithms results in smarter technology as well as improved patient experiences. Most AI applications in healthcare use "augmented intelligence," which curates the algorithms' output to provide clinicians with direction on "where to look" when they get the analysis.


AI 100: The most promising artificial intelligence startups of 2022 - CB Insights Research

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The AI 100 is CB Insights' annual list of the 100 most promising private AI companies in the world. This year's winners are working on diverse solutions designed to recycle plastic waste, improve hearing aids, combat toxic online gaming behavior, and more. CB Insights has unveiled the winners of the sixth annual AI 100 -- a list of the 100 most promising private AI companies across the globe. Some of this year's winners are advancing the development and use of artificial intelligence (AI) across specific industries -- such as healthcare, gaming, and agriculture. On the other hand, some are developing applications to support sales, engineering design, cybersecurity, and other functions across a wide range of industries.


The Spendy Somnox 2 Robot Sends You to Slumberland

WIRED

As I lie in bed spooning my wee robot, one hand on its gently undulating belly as it slows my breathing, I'm struck by the memory of co-sleeping with my kids when they were babies. It can be soothing to share your bed. Research suggests we report better sleep when bed-sharing, even when objective measures reveal sleep quality has worsened. Somnox 2 is a limbless bean-shaped torso designed to gradually slow your breathing, as you unconsciously match its rhythm. It can adjust to your breathing rate to calm you and help you drop off.


New imaging method makes tiny robots visible in the body

Robohub

How can a blood clot be removed from the brain without any major surgical intervention? How can a drug be delivered precisely into a diseased organ that is difficult to reach? Those are just two examples of the countless innovations envisioned by the researchers in the field of medical microrobotics. Tiny robots promise to fundamentally change future medical treatments: one day, they could move through patient's vasculature to eliminate malignancies, fight infections or provide precise diagnostic information entirely noninvasively. In principle, so the researchers argue, the circulatory system might serve as an ideal delivery route for the microrobots, since it reaches all organs and tissues in the body.