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Council Post: How AI Will Drive The Precision Health Research Revolution Through 2030

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Praduman Jain is CEO and founder of Vibrent Health, a digital health technology company powering the future of precision medicine. There has been quite a bit of hype over the last several years about how artificial intelligence (AI) would transform health care. Translating the predictive power of AI algorithms into research methods and clinical practice, however, has proved challenging, which inevitably leads to disillusionment. But rather than getting frustrated with AI and machine learning, I would argue that strategic and ethical deployment of artificial intelligence will, by necessity, be central to the success of precision health research over the next decade. Several factors are coming together to make AI more critical to progress.


How AI simplifies data management for drug discovery

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Calithera is running registered clinical trials on its products to study their safety, whether they're effective in patients with specific gene mutations, and how well they work in combination with other therapies. The company must collect detailed data on hundreds of patients. While some of its trials are in early stages and involve only a small number of patients, others span more than 100 research centers across the globe. "In the life-sciences world, one of the biggest challenges we have is the enormous amount of data we generate, more than any other business," says Behrooz Najafi, Calithera's lead information technology strategist. Calithera must store and manage the data while making sure it's readily available when needed, even years from now.


How AI simplifies data management for drug discovery

#artificialintelligence

Calithera is running registered clinical trials on its products to study their safety, whether they're effective in patients with specific gene mutations, and how well they work in combination with other therapies. The company must collect detailed data on hundreds of patients. While some of its trials are in early stages and involve only a small number of patients, others span more than 100 research centers across the globe. "In the life-sciences world, one of the biggest challenges we have is the enormous amount of data we generate, more than any other business," says Behrooz Najafi, Calithera's lead information technology strategist. Calithera must store and manage the data while making sure it's readily available when needed, even years from now.


Council Post: Three Ways Artificial Intelligence Is Changing Healthcare – And Six Principles To Ensure Its Success

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Artificial Intelligence (AI) has been used to revolutionize healthcare for some time, but all the signs suggest that AI is poised to completely take the medicinal world by storm over the next few years. Governments -- from the U.K. and the U.S. to Germany, Qatar and China -- have published strategy papers on AI in healthcare and backed up their words with multimillion-dollar funding. The WHO recently issued its first global report on AI in healthcare. And global equity funding in AI health start-ups has risen quarter on quarter since the end of 2019, reaching a record $2.5 billion in the first quarter of 2021. All this buzz is a reflection of the enormous range of healthcare-related activities in which AI can have a big impact.


AI Makes Strangely Accurate Predictions From Blurry Medical Scans, Alarming Researchers

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New research has found that artificial intelligence (AI) analyzing medical scans can identify the race of patients with an astonishing degree of accuracy, while their human counterparts cannot. With the Food and Drug Administration (FDA) approving more algorithms for medical use, the researchers are concerned that AI could end up perpetuating racial biases. They are especially concerned that they could not figure out precisely how the machine-learning models were able to identify race, even from heavily corrupted and low-resolution images. In the study, published on pre-print service Arxiv, an international team of doctors investigated how deep learning models can detect race from medical images. Using private and public chest scans and self-reported data on race and ethnicity, they first assessed how accurate the algorithms were, before investigating the mechanism.


Optellum, Johnson & Johnson Collaborate on AI-Powered Lung-Health Initiative

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This announcement accelerates Optellum's market entry, building on its FDA clearance earlier this year and deployments underway at hospitals in the USA and ongoing clinical trials in the United Kingdom. It identifies and tracks at-risk patients and assigns a Lung Cancer Prediction score to lung nodules: small lesions, frequently detected in chest Computed Tomography (CT) scans that may or may not be cancerous. The Optellum AI will be used to drive accurate early diagnosis and optimal treatment decisions with the aim of treating patients earlier, potentially at a pre-cancerous stage, increasing survival rates.


Utilizing Artificial Intelligence in Critical Care: Adding A Handy Tool to Our Armamentarium

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As per the definition found in Britannica by Copeland, AI is commonly referred to as a computer system with human intellectual features, e.g., reasoning, discovering, generalizing, and learning from prior exposure [1,2]. U.S Food and Drug Administration (US-FDA) has also stated in 2019 that AI has the potential to transform the healthcare industry by its ability to derive new information from the vast dataset that feeds into it [2,3]. Machine learning (ML) can be simply understood as a subset of an application of AI in which machines analyze and use a large dataset to produce unique algorithms capable of "statistical learning" as described by Gutierrez [2]. The use of ML has surged in critical care in the field of the discovery of drugs, diagnostic tools, medical imaging, and therapeutics amongst others. It can potentially help us better understand the vast set of data available to us in an intensive care unit (ICU) and apply it to tackle a multitude of medical conditions [2,4]. ML can be divided into two main models based on learning tasks, which are supervised and unsupervised learning algorithms.


Should Parents Stock Up on At-Home COVID Tests?

Slate

He's 11-years-old and, until he can receive his shots, Gronvall's been using at-home COVID-19 test kits in order to determine if his sniffles are more than allergies or a slight cold. The test swabs are longer than a Q-tip, but easier on the nasal cavity than a flu diagnostic or the original "brain swab" used to test for COVID since early in the pandemic. "There's often a lot of stuff coming out of their nose," Gronvall said of her kids, with a slight chuckle, when we talked recently. As an associate professor at the Johns Hopkins Bloomberg School of Public Health, Gronvall knows the importance of testing. "We can't all rely on everybody being extra scrupulous and paying attention to all of the COVID restrictions," she said.


Artificial Intelligence Aids in Discovery of New Prognostic Biomarkers for Breast Cancer

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Scientists at Case Western Reserve University have used artificial intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment -- and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role -- using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.


Artificial intelligence could be new blueprint for precision drug discovery

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Writing in the July 12, 2021 online issue of Nature Communications, researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval. The study findings could measurably change how researchers sift through big data to find meaningful information with significant benefit to patients, the pharmaceutical industry and the nation's health care systems. "Academic labs and pharmaceutical and biotech companies have access to unlimited amounts of'big data' and better tools than ever to analyze such data. However, despite these incredible advances in technology, the success rates in drug discovery are lower today than in the 1970s," said Pradipta Ghosh, MD, senior author of the study and professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine. "This is mostly because drugs that work perfectly in preclinical inbred models, such as laboratory mice, that are genetically or otherwise identical to each other, don't translate to patients in the clinic, where each individual and their disease is unique. It is this variability in the clinic that is believed to be the Achilles heel for any drug discovery program."