A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world.

DUBLIN--(BUSINESS WIRE)--The "Asia-Pacific Artificial Intelligence (AI) in Drug Discovery Market 2020-2026 by Offering, Technology, Drug Type, Therapeutic Area, Application, End User, and Country: Trend Forecast and Growth Opportunity" report has been added to ResearchAndMarkets.com's offering. Asia-Pacific artificial intelligence (AI) in drug discovery market will grow by 33.2% over 2020-2026 with a total addressable market cap of $2.78 billion, owing to fast adoption of AI technology in pharmaceutical industry and drug development. The report provides historical market data for 2015-2019, revenue estimates for 2020, and forecasts from 2021 till 2026. Highlighted with 34 tables and 53 figures, this 121-page report is based on a comprehensive research of the entire Asia Pacific AI in drug discovery market and all its sub-segments through extensively detailed classifications. Profound analysis and assessment are generated from premium primary and secondary information sources with inputs derived from industry professionals across the value chain.

Boston and San Francisco, January 7, 2020 – Pear Therapeutics, Inc., the leader in Prescription Digital Therapeutics (PDTs), announced today that it has entered into agreements with multiple technology innovators, including Firsthand Technology, Inc., leading researchers from the Karolinska Institute in Sweden, Cincinnati Children's Hospital Medical Center, Winterlight Labs, Inc., and NeuroLex Laboratories, Inc. These new agreements continue to bolster Pear's PDT platform, by adding to its library of digital biomarkers, machine learning algorithms, and digital therapeutics. Pear's investment in these cutting-edge technologies further supports its strategy to create the broadest and deepest toolset for the development of PDTs that redefine standard of care in a range of therapeutic areas. With access to these new technologies, Pear is positioned to develop PDTs in new disease areas, while leveraging machine learning to personalize and improve its existing PDTs. "We are excited to announce these agreements, which expand the leading PDT platform," said Corey McCann, M.D., Ph.D., President and CEO of Pear.

BOSTON & SAN FRANCISCO--(BUSINESS WIRE)--Pear Therapeutics, Inc., the leader in Prescription Digital Therapeutics (PDTs), announced today that it has entered into agreements with multiple technology innovators, including Firsthand Technology, Inc., leading researchers from the Karolinska Institute in Sweden, Cincinnati Children's Hospital Medical Center, Winterlight Labs, Inc., and NeuroLex Laboratories, Inc. These new agreements continue to bolster Pear's PDT platform, by adding to its library of digital biomarkers, machine learning algorithms, and digital therapeutics. Pear's investment in these cutting-edge technologies further supports its strategy to create the broadest and deepest toolset for the development of PDTs that redefine standard of care in a range of therapeutic areas. With access to these new technologies, Pear is positioned to develop PDTs in new disease areas, while leveraging machine learning to personalize and improve its existing PDTs. "We are excited to announce these agreements, which expand the leading PDT platform," said Corey McCann, M.D., Ph.D., President and CEO of Pear.

At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious "will AI replace doctors/drug developers/occupation X?" discussion, and are poised to enter the more considered conversation of "Where will AI be useful?" As I've watched this evolution in both drug discovery and medicine, I've come to appreciate that in addition to the many technical barriers often considered, there's a critical conceptual barrier as well – the threat some AI-based approaches can pose to our "explanatory models" (a construct developed by physician-anthropologist Arthur Kleinman, and nicely explained by Dr. Namratha Kandula here), our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that's unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding. A physician examines her patient and tries to integrate her observations – what she sees, feels, hears, and is told – and what she learns from laboratory and radiological tests – sodium level, CT scans – to formulate an understanding of what's wrong with her patient, and to fashion a treatment approach. The idea is that this process of understanding of what's wrong and developing a therapeutic plan is fundamentally rooted in science.

These diseases attract a ton of research effort and funding, and for good reason: They afflict tens of millions of people each year. But there are about 7,000 known rare diseases that rarely get attention. Also called "orphan" diseases, these conditions collectively affect about 400 million worldwide and were historically neglected by the drug industry, which could not justify the costs of developing drugs to address the small number of affected patients. Salt Lake City-based Recursion Pharmaceuticals focuses on drug discovery across several therapeutic areas, including hundreds of rare diseases that currently lack treatments -- such as Sandhoff disease, an inherited, often-fatal disorder that destroys neurons in an infant's brain and spinal cord. The condition affects less than 1 in 100,000 people in Europe.

Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn't look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn't follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. Getting a car to drive this way was an impressive feat.

Artificial intelligence (AI) was a key topic at both MEDICA and the RSNA conference this year. But what are its applications in healthcare in general and radiology in particular? And what are the barriers? Dr. Michael Forsting, director of the Institute of Diagnostic and Interventional Radiology and Neuroradiology at Essen University Hospital in Germany talked to MedicalExpo e-magazine about his experiences with AI. MedicalExpo e-magazine: What are the major challenges facing AI in healthcare?

Earlier this year, artificial intelligence scientist Sebastian Thrun and colleagues at Stanford University demonstrated that a "deep learning" algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist. The cancer finding, reported in Nature, was part of a stream of reports this year offering an early glimpse into what could be a new era of "diagnosis by software," in which artificial intelligence aids doctors--or even competes with them. Experts say medical images, like photographs, x-rays, and MRIs, are a nearly perfect match for the strengths of deep-learning software, which has in the past few years led to breakthroughs in recognizing faces and objects in pictures. Companies are already in pursuit. Verily, Alphabet's life sciences arm, joined forces with Nikon last December to develop algorithms to detect causes of blindness in diabetics.

Australian experts have spoken out about a recent US study that claimed to show artificial intelligence can identify people with suicidal thoughts - by analysing their brain scans. It sounds promising - but it's worth pointing out only 79 people were studied, so are the results enough to show this is a path worth pursing? The research, published in Nature, studied brain activity in subjects when presented with a number of different words - like death, cruelty, trouble, carefree, good and praise. A machine-learning algorithm was then trained to see the nureal response differences between the two groups involved - those with suicidal thoughts, and those with non-suicidal thoughts. And it showed promise - the algorithm correctly identified 15 of 17 patients as belonging to the suicide group, and 16 of 17 healthy individuals as belonging to the control group.