A researcher preparing to perform an intraperitoneal injection on mice.Marcos del Mazo/LightRocket/Getty This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. Animal testing has long been necessary for a drug to gain approval by the US Food and Drug Administration--but it may be on its way out. A new law seeks to replace some lab animal use with high-tech alternatives. The FDA Modernization Act 2.0, signed by President Biden at the end of December with widespread bipartisan support, ends a 1938 federal mandate that experimental drugs must be tested on animals before they are used in human clinical trials. While the law doesn't ban animal testing, it allows drugmakers to use other methods, such as microfluidic chips and miniature tissue models, which use human cells to mimic certain organ functions and structures.

SmartCardia 7L Patch is a breakthrough 7/14 day patch that offers real-time 7-Lead ECG and vitals with AI SaaS* *SmartCardia solution approved as SCaAI patch and cloud platform in Europe (CE Class IIa) - ECG, respiration, SpO2, activity and cloud based arrhythmia detection.

The adoption of artificial intelligence (AI) and machine learning (ML) has been one of the fastest growing trends across industries over the past decade. With the continuous advancements in technology, access to ever more powerful computers, increased availability of clinical and research data, and rapid development of novel algorithms that analyze and utilize that data, interest in applying AI and ML to trial design and clinical trials to improve high failure rates is increasing. Among its many potential practical applications, AI and ML can be used to minimize errors in clinical trial participant management (e.g., cohort selection, patient identification and recruiting, participant retention) and streamline data management (e.g., automate data collection, monitor data quality, analyze large data sets).1 However, realizing the potential of this technology will require overcoming a range of different issues, including problems with data quality and access, transparency of underlying development and validation processes, potential bias inherent in the source data as well as the algorithm's implementation, and the lack of definitive regulatory guidance from the relevant government agencies. Selecting and recruiting patients for clinical trials is complicated and, despite the extensive time and effort companies put into clinical trial participant management, one of the biggest factors that causes a clinical trial to fail is failure to select and recruit the most suitable subjects for a trial.2

Although artificial intelligence is entering health care with great … In technical terms, what we're describing is called a machine learning …

Boston-based Activ Surgical designed ActivSight to provide enhanced visualization and real-time on-demand surgical insights in the operating room. The easy-to-adapt model seamlessly attaches to laparoscopic and robotic systems. Activ Surgical received CE mark approval for ActivSight in December 2022. The FDA cleared the technology in April 2021. The company aims to transform the surgical experience using emerging technologies and data.

In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled data sets. Although transfer learning can be advantageous, it requires the existence of a related large data set. This work introduces three graph-based models incorporating Merriman-Bence-Osher (MBO) techniques to tackle this challenge. Specifically, graph-based modifications of the MBO scheme are integrated with state-of-the-art techniques, including a home-made transformer and an autoencoder, in order to deal with scarcely-labeled data sets. In addition, a consensus technique is detailed. The proposed models are validated using five benchmark data sets. We also provide a thorough comparison to other competing methods, such as support vector machines, random forests, and gradient boosting decision trees, which are known for their good performance on small data sets. The performances of various methods are analyzed using residue-similarity (R-S) scores and R-S indices. Extensive computational experiments and theoretical analysis show that the new models perform very well even when as little as 1% of the data set is used as labeled data.

As 2022 comes to an end, EE Times is highlighting memorable stories from each of its editors over the past year. Today's spotlight is on Sally Ward-Foxton, a correspondent at EE Times. Sally covers AI topics for EE Times and the EE Times Europe magazine. She has spent the last 18 years writing about the electronics industry from London, U.K., and has written for Electronic Design, ECN, Electronic Specifier: Design, Components in Electronics, and many more. She holds a master's degree in Electrical and Electronic Engineering from the University of Cambridge.

This process is painful, time-consuming, and expensive. Today, we already use ML to perform image segmentation to not only isolate the location of lesions, but to also reconstruct a high fidelity 3D model of a patient's arteries. In other words, ML is already playing an essential role in identifying and building the right geometry. Machine learning can also speed up segmentation for particularly complicated diseases, such as aortic dissection, which can easily take eight or nine hours for a trained person. Using ML, however, segmentation might take just a few minutes.

In light of a pending national standard requiring breast density notification in mammography reports, an emerging artificial intelligence (AI) tool may help reduce subjectivity and variability in breast density assessments. The Food and Drug Administration (FDA) has granted 510(k) clearance for Saige-Density (DeepHealth/RadNet), an adjunctive AI software that provides automated categorization of breast density based on the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification. DeepHealth said a retrospective, multicenter study showed a 91.5 percent alignment between Saige-Density assessment and consensus assessment of breast density by five specialists in breast imaging. The Saige-Density AI algorithm was trained on a racially diverse database of over 166,000 images from 30,000 mammography exams across the United States, according to DeepHealth. "Achieving FDA clearance for another important tool in the breast cancer screening process in such a short time frame highlights our aggressive commitment to bringing state-of-the art AI innovation to the breast screening mammography market," noted Gregory Sorenson, M.D., the CEO and co-founder of DeepHealth.

My friends in pharma may like this - the world's first documentary video hackathon covering the discovery of a novel medicine from the development of AI platform to using this AI platform to discover a novel target, generate novel molecule and take it all the way into the human clinical trials. The target was discovered using aging research and it may be the first aging-clock derived therapeutic. We started recording the footage in 2020 and generated over 160 hours worth of footage material, interviews, laboratory experiments, internal presentations, successes, failures, daily life of deeply committed scientists - all on tape. We are now offering 2 years of footage to the documentary video experts to take part in the global competition to tell the story and explain how novel medicines are made. We have a panel of celebrity judges and great prizes for the winners.