In a recent study published in the American Journal of Gastroenterology, researchers at Cedars-Sinai Medical Center in the United States evaluated an artificial intelligence (AI)-based smartphone application (app) trained to assess a patient's stool characteristics. Study: A Smartphone Application Using Artificial Intelligence Is Superior To Subject Self-Reporting When Assessing Stool Form. Functional gastrointestinal (GI) disorders, especially luminal ones, require that a patient self-report stool form and frequency. However, since the symptoms of diarrhea common in irritable bowel syndrome with diarrhea (IBS-D) patients are subjective, the inability to accurately report or assess stool form and frequency makes it challenging to determine the effectiveness of therapeutic interventions in these conditions. The Bristol Stool Scale (BSS) is the United States Food and Drug Administration (US-FDA) approved 7-point scale that ranks stool consistency from 1 (hard lumps) to 7 (liquid).

Researchers have noted that traumatic fractures are among the most commonly missed diagnoses.1,2 However, a new study suggests that artificial intelligence (AI) may have significant benefit in improving the assessment of fractures.3 In the study of 500 patients (268 men and 232 women), researchers compared unassisted assessment of acute fractures versus assessment with the assistance of an FDA-cleared algorithm (Boneview, Gleamer) and stand-alone use of AI. The authors found that AI assisted assessment had a 20 percent higher sensitivity (86 percent) of diagnosing fractures on radiographs in comparison to unassisted assessment (66 percent). The use of AI assistance led to a lower number of false negatives (26) in comparison to unassisted radiograph assessment (64), according to the study.

Just as many people have replaced their manual toothbrush with an electric one, so too could robots usher in a new era of teeth cleaning. Scientists have created a swarm of shapeshifting microrobots that they claim can brush, rinse and floss your teeth all at the same time. In a proof-of-concept study, researchers from the University of Pennsylvania showed that the hands-free system could effectively automate the treatment and removal of tooth-decay-causing bacteria and dental plaque. The system could be particularly valuable for those who lack the manual dexterity to clean their teeth effectively themselves, the experts said. The building blocks of these microrobots are iron oxide nanoparticles which have both catalytic and magnetic activity.

Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning.

Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines. We finetuned a deep learning classifier using a state-of-the-art model, XLNet, to detect each tweet's sentiment automatically. We employed validated methods to extract the users' race or ethnicity, gender, age, and geographical locations from user profiles. Incorporating multiple data sources, we assessed the sentiment patterns among subpopulations and juxtaposed them against vaccine uptake data to unravel their interactive patterns. 11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users over two years were analyzed. The finetuned model for sentiment classification yielded an accuracy of 0.92 on testing set. Users from various demographic groups demonstrated distinct patterns in sentiments towards COVID-19 vaccines. User sentiments became more positive over time, upon which we observed subsequent upswing in the population-level vaccine uptake. Surrounding dates where positive sentiments crest, we detected encouraging news or events regarding vaccine development and distribution. Positive sentiments in pregnancy-related tweets demonstrated a delayed pattern compared with trends in general population, with postponed vaccine uptake trends. Distinctive patterns across subpopulations suggest the need of tailored strategies. Global news and events profoundly involved in shaping users' thoughts on social media. Populations with additional concerns, such as pregnancy, demonstrated more substantial hesitancy since lack of timely recommendations. Feature analysis revealed hesitancies of various subpopulations stemmed from clinical trial logics, risks and complications, and urgency of scientific evidence.

Stanford, CA, June 16, 2022: CARPL.ai, a technology platform that connects Artificial Intelligence (AI) applications... CARPL - the world's first testing and deployment platform for radiology automation has recently been... Accelerating model validation and clinical adoption of AI solutions built by Thomas Jefferson University using... CARPL is the world's first end-to-end platform for the development, testing and deployment of medical imaging AI Incubated at India's leading diagnostics provider, CARPL works with 40 HCPs, AI Developers and Med Tech Companies

Artificial intelligence's (AI) applicability in cardiac imaging is rapidly growing and was a major topic of discussion at this year's EuroPCR 2022 meeting. Many session speakers discussed how they are using AI tools in their day-to-day practice and in their research to improve decision-making and patient/research outcomes. It's no secret, however, that AI tools are only as good as the data sets and the thousands of expert opinions used to power them. Implementing AI applications in our day-to-day practice, from an operations standpoint, could mean adjusting clinician workflows and setting aside time to set up and train on the new systems. And from an efficacy standpoint, it leaves clinicians wary of result accuracy, especially if they are unsure how good the data used to power the technology really is.

With the growth of artificial intelligence and machine learning in healthcare, even prosthetic limbs are becoming smart. These smart prosthetics can combine manual control with machine learning for more accessible and effective use. We are seeing a growth of machine learning in healthcare, where it is used to improve a patient's overall health, including providing accurate diagnosis and better treatment plans. Additionally, machine learning (ML) can also understand healthcare data by improving diagnostics and predicting accurate outcomes. One of the latest fields where AI and ML have been making an impact is prosthetics.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) is making a big splash in enterprise applications across the board, but most of the attention has gone to public-facing functions like chatbots and personal or professional assistants. But this masks the fact that much of the real action is taking place behind the scenes, in the myriad back office processes that contribute to the costs and complexities of running a modern organization. AI is not like previous generations of technology, however, which were targeted at specific operations and crafted to operate in a predefined manner. Instead, the challenge for the enterprise is to create the kind of training and development processes that allow AI to place traditionally manual processes under fully automated control – essentially assuming the day-to-day work of running the office while the labor force focuses on continued optimization and strategic development.

Can you Currently buy a Juul e-cigarette? That depends on what day of the week it is. Earlier this week the FDA denied marketing authorization for Juul, which first started selling its e-cigarettes in 2015 (though it has operated under various company names since 2007). The FDA said the reason for the denial was that Juul "failed to provide sufficient toxicology data to demonstrate the products were safe," ArsTechnica reports, and as such the agency could not complete its toxicology assessment. The FDA specifically pointed to "potentially harmful chemicals leaching from the company's proprietary e-liquid pods" as a concern.