How healthcare is evolving with the help of AI/ML? Data analysis and improve diagnosis: AI-enabled technology can evaluate data at a faster rate than that of any human, which include clinical tests, medical history, and genetic analysis that can aid healthcare professionals in determining a patient's condition. Administrative and routine tasks: Routine tasks such as record keeping, data management, and scan analysis, can be automated with AI. Without much time being spent on manual processes, healthcare professionals can allocate more resources to patient care. Drug Development: Developing a new drug takes a lengthy time.

According to the World Cancer Day the key cancer facts are that 10 million people die from cancer every year, at least one third of common cancers are preventable, cancer is the second-leading cause of death worldwide, 70% of cancer deaths occur in low-to-middle income countries, millions of lives could be saved each year by implementing strategies for prevention, early detection and treatment, and the total annual economic cost of cancer is estimated at $ 1.16 trillion. For all the above reasons, AI and ML techniques are breaking into cancer research and oncology, where the potential applications are vast, and include early detection and diagnosis of cancer, subtype classification of cancer, optimisation of cancer treatment and identification of new therapeutic targets. In particular, AI is predicted to change cancer health care by advancing clinical research and drug development. And besides cutting costs, improving trial quality and reducing trial times by almost half, AI is predicted to find novel cancer biomarkers and gene signatures, recruit eligible clinical trial patients in minutes and read volumes of text in seconds. Moreover, breakthrough discoveries involving new diagnostic tools for cancer have seen AI as a major player. The phrase "prevention is better than cure" is often attributed to the Dutch philosopher Desiderius Erasmus in around 1500, but prevention is now synonym to "it's cheaper too", since preventing future illnesses and complications is vital to the future sustainability of health systems and households as well.

Air Recon DL's benefits extend to nearly all magnetic resonance imaging (MRI) clinical procedures. The platform covers all anatomies, enabling better image quality, shorter scan times and improved patient experience. GE Healthcare said in a news release that the solution's compatibility expands from 2D to 3D imaging sequences. This allows physicians to diagnose patients with an improved signal-to-noise ratio (SNR) and sharpness. Meanwhile, it said the 3D imaging provides for more clinical efficiency by eliminating the need for multiple 2D acquisitions.

In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has been studied for its utility in virtual screening and lead optimization. Recently, machine learning (ML) methods using experimental data has been popular because physics-based methods like quantum mechanics and molecular dynamics are not suitable for high-throughput tasks due to its computational costs. However, ML method can exhibit over-fitting problem in a data-deficient condition, and this is the case for most chemical property datasets. In addition, ML methods are regarded as a black box function in that it is difficult to interpret contribution of hidden features to outputs, hindering analysis and modification of structure-activity relationship. To deal with mentioned issues, we developed Bayesian graph neural networks (GNNs) with the self-attention readout layer. Unlike most GNNs using self-attention in node updates, self-attention applied at readout layer enabled a model to improve prediction performance as well as to identify atom-wise importance, which can help lead optimization as exemplified for three FDA-approved drugs. Also, Bayesian inference enables us to separate more or less accurate results according to uncertainty in solubility prediction task We expect that our accurate, reliable and interpretable model can be used for more careful decision-making and various applications in the development of drugs.

White Shark Media, a leading digital marketing agency specializing in pay-per-click advertising, announced the launch of AdClicks, a new reporting software for marketing agencies and freelancers. Designed for marketing experts, by marketing experts, this new tool provides professionals with a single dashboard that allows them to better serve their clients with a one-stop-shop for all of their reporting needs. AdClicks helps marketing agencies and freelancers achieve their business goals with accurate data, performance recommendations and engaging client reporting from a single dashboard. "Innovation is at the heart of our work at White Shark Media, and we are always looking for creative solutions to some of marketing's biggest challenges," said Alexander Nygart, CEO of White Shark Media. "We are so excited to launch this new brand under White Shark Media and help marketers instantly, accurately and securely track their client's marketing data."

Alphabet subsidiary and precision health company Verily recently announced a breakthrough in its AI drug discovery GPCR research collaboration with Sosei Heptares. A mere six months ago Verily launched the study with Sosei Heptares – a global leader in GPCR structure-based drug design – with an aim to "prioritise protein targets for therapeutic targeting in immune-mediated disease". Now, Verily has announced that early results from its "next generation immune mapping technology" Immune Profiler platform have already identified "more effective therapeutic options against G protein-coupled receptors (GPCR) in autoimmune and other immune-mediated diseases". The companies hope that in the year to come those data targets will be entered for validation, hit generation, and lead selection. With approximately one third of all current FDA-approved drugs targeting GPCRs, Verily/Sosei Heptares are looking to expedite GPCR research within not only immunology, but also gastroenterology and immuno-oncology as well, and the latest data bodes well for future development of therapeutic options in these areas.

Did you know: Representative Data We'd like to point out some key statistics from our last post on small study sizes. First of all, the question this article is trying to respond is about the prevalence and extent of small study effects in diagnostic imaging. Reach out to us to know how you can have quick access to millions of diverse medical imaging data and avoid data bias: https://lnkd.in/gVwPPXUB

"We do not want schizophrenia researchers knowing a lot about software engineering," said Amy Winecoff, data scientist and Princeton's Centre for IT Policy. Research asserts that a basic understanding of machine learning and other software engineering principles might be a desirable trait for medical practitioners, but these skills should not come at the expense of expertise in domain knowledge. Many new startups and enterprises sell their products boasting about incorporating AI/ML techniques in the development. Though this is an issue in the developer and business market, the bigger worry is misapplied AI/ML algorithms in the field of science and healthcare as it causes real world consequences. Sayash Kapoor and Arvind Narayanan of Princeton University published a research paper--Leakage and the Reproducibility Crisis in ML-based Science, pointing out the problem of "data leakage" in various researches using pools of data to train and test their development's performance.

I'm a musician and serial entrepreneur, was trained as classical pianist, and studied for a master's in music and philosophy. As a business executive, I have over 20 years of experience, leading several innovations from inception and product strategy to development and product management, to commercialization in global markets. My track record includes closing successful deals with global vendors, mobile operators, and content companies, as well as winning global awards and prizes. Across the past five years, Amit Sternberg has been driving innovation around music, health and technology. Sternberg's newest company, Rubato, is currently in its seed stage.

U.S. Army veteran Richard Neider is able to walk again after suffering a spinal-cord injury in Iraq thanks to a robotic exoskeleton. Neider, who is the first veteran in Phoenix to receive the ReWalk Personal 6.0 Exoskeleton through a Veterans Affairs program, was unable to walk after being injured in an explosion in Iraq and has spent almost ten years in a wheelchair. The battery-powered, motorized device has sensors that detect when he shifts his weight and then tells Neider's other leg to move - creating a walking motion. The light exoskeleton helps to stabilize his knees and hip. 'I can't ever stop smiling when I'm in it,' the former Army sergeant, who saw his May 2005 injury worsen over time, tells AZFamily.