Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains the state of artificial intelligence (AI) in radiology in 2022. Although there are about 200 AI algorithms for medical imaging now cleared by the U.S. Food and Drug Administration (FDA), a recent ACR survey of its members showed AI only has about a 2% market penetration rate. "So, there is about another 98% that fall into the category of potential addressable market," Dreyer said. "Now why is that when there is a lot of enthusiasm and we are past the days from six years ago when radiologists were fearful of losing their jobs to AI because Geoffrey Hinton said we should stop training radiologists because AI will take over in another 5 years. That was in 2016, and are now past the five-year mark and it's ridiculous, because today there is an incredible shortage of radiologists."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! In 2018, Iowa-based Digital Diagnostics made headlines when it became the first autonomous AI (artificial intelligence) system authorized by the U.S. Food and Drug Administration. It received FDA approval to use AI to autonomously detect diabetic retinopathy in adults with diabetes, without the need for input from a doctor. Its AI-diagnostic system, the IDx-DR, can 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 edema.

Retrace, a leader in dental artificial intelligence and provider of digital infrastructure for U.S. healthcare, announces the publication, "A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level" in the August 2022 Edition of the Journal of Dentistry. This groundbreaking study for the first time demonstrates how the use of a novel Generative Adversarial Network (GAN), (U.S. Patent Numbers: US 11,217,350 B2; US 11,276,151 B2; US 11,398,013 B2), often referred to as a "Deep Fake", improves the diagnostic accuracy of AI algorithms in identifying periodontal disease. Medical and dental AI imaging algorithms are often trained on limited data sets from a limited number of providers, patients and imaging sources. As a result, when these algorithms are used in a general production environment, the algorithms struggle to achieve the same level of accuracy as the environment they were trained in. "Over the past few years, we have seen a sharp rise in dental and medical imaging AI companies; some who have even received FDA Clearance," said Dr. Ali Sadat, Founder and CEO of Retrace.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Tech startups are going through tough times as a result of a slowdown in growth capital. Investment firms are advising their portfolio companies to extend their runway. Companies are suffering from valuation markdowns and resorting to layoffs to cut costs.

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages.

Elon Musk is reportedly looking at a potential investment deal between Neuralink and brain-computer startup Synchron that successfully implanted a chip into a severely paralyzed ALS patient in July. Four people who work or have worked at Neuralink told Reuters that Musk has expressed disappointment at the slow pace of progress on the company's brain implant device and recently approached the CEO of Synchron about a possible deal. Brooklyn-based Synchron made history when it implanted a 1.5-inch long brain-computer interface (BCI) called a Stentrode into a patient's brain without the need for cutting into their skull - by accessing the brain via blood vessels. In contrast, Neuralink's device, which is being tested on monkeys, requires surgery to make a small incision to implant it. Four people who work or have worked at Neuralink told Reuters that Musk has expressed disappointment at the slow pace of progress on the company's brain implant device, called the Link (seen above) Neuralink's device, which is being tested on monkeys, requires surgery to make a small incision to implant it, but Synchron's device does not require surgery.

Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery. CandidateDrug4Cancer dataset encompasses multiple most-mentioned 29 targets for cancer, covering 54869 cancer-related drug molecules which are ranged from pre-clinical, clinical and FDA-approved. Besides building the datasets, we also perform benchmark experiments with effective Drug Target Interaction (DTI) prediction baselines using descriptors and expressive graph neural networks. Experimental results suggest that CandidateDrug4Cancer presents significant challenges for learning molecular graphs and targets in practical application, indicating opportunities for future researches on developing candidate drugs for treating cancers.

"Today, we live in that quadrant of things humans can do and humans are supervising," Dreyer explained. "That is all the [U.S. Food and Drug Administration (FDA)] approved AI stuff that we see today." He said the next step is for AI to move into the realm of superhuman work, such as measuring 1,000 lymph nodes at once, or to make a risk prediction about future events in the next two years based on the patient's prior 40 images, because it looks like a million other patients' scans. Dreyer said the FDA is in discussions with vendors on fully autonomous AI for radiology applications, but the agency wants to see controls built into the software.

Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. In this work, we provide statistical evidence for the result promulgation influence on the public pharma market value. Whereas most works focus on retrospective impact analysis, the present research aims to predict the numerical values of announcement-induced changes in stock prices. For this purpose, we develop a pipeline that includes a BERT-based model for extracting sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. The challenge of the problem lies in inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to the negative news. Moreover, such phenomenon as the drop in stocks after the positive announcements affirms the counterintuitiveness of the price behavior. Importantly, we discover two crucial factors that should be considered while working within a predictive framework. The first factor is the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of small drug diversification. The second one is the network effect of the events related to the same company or nosology. All findings and insights are gained on the basis of one of the biggest FDA (the Food and Drug Administration) announcement datasets, consisting of 5436 clinical trial announcements from 681 companies over the last five years.