For the past six years, Elon Musk has been working on a chip designed to be implanted into human brains, with his neurotechnology company Neuralink. His ultimate goal is to develop a'brain-computer interface' that will initially be used to help people with paralysis or motor neurone disease to communicate. It will allegedly allow them to operate computers and mobile devices using their thoughts, but could have further uses in years to come. So what exactly is the chip? How does it work and how will it cure all medical problems?

It's been six years since Tesla, SpaceX (and now Twitter) CEO Elon Musk co-founded brain-control interfaces (BCI) startup, Neuralink. It's been three years since the company first demonstrated its "sewing machine-like" implantation robot, two years since the company stuck its technology into the heads of pigs -- and just over 19 months since they did the same to primates, an effort that allegedly killed 15 out of 23 test subjects. After a month-long delay in October, Neuralink held its third "show and tell" event on Wednesday where CEO Elon Musk announced, "we think probably in about six months, we should be able to have a Neuralink installed in a human." Neuralink has seen tumultuous times in the previous April 2021 status update: The company's co-founder, Max Hodak, quietly quit just after that event, though he said was still a "huge cheerleader" for Neuralink's success. That show of confidence was subsequently shattered this past August after Musk reportedly approached Neuralink's main rival, Synchron, as an investment opportunity.

"AI algorithms have made it through the FDA approval process, and people are now looking at and trying to figure out how to build them into their clinical practice and what the economics of it are, what makes these worth while and what adds value," Kahn explained. "One of the challenges is, what do you want these things to do? What role do they fill?" While more than 300 AI algorithms are now cleared by the FDA, and a large number of these are in radiology, radiologists need to determine what is useful to their practice. "For things in radiology, it has to improve the productively of the radiologist," Kahn said.

This Digital Health News industry roundup includes an IT award for the Department of Health and Social Care and Netcompany for the NHS Covid Pass, accreditation for an AI device and a virtual falls service keeping care home residents out of hospital. Digital health and AI company Empatica has received clearance of its Empatica Health Monitoring Platform by the US Food and Drug Administration (FDA). The platform has been cleared for continuous data collection to monitor blood oxygen saturation during rest, peripheral skin temperature, activity associated with movement during sleep and electrodermal activity. Each digital biomarker is based on trained algorithms that analyse sensor data in one-minute intervals. Dr. Marisa Cruz, chief medical officer of Empatica, said: "This clearance represents a significant step forward for our scientific community. Patients, healthcare providers, and researchers deserve digital health products that are accurate, validated in diverse populations, and intuitive to use. "We are proud to have built a solution that accomplishes these goals, offering a high-quality and reliable digital health tool to scientists working to improve patient outcomes through research and clinical care." The company has also announced the closing of its Series B financing. The investment was led by Sanofi Venture and RA Capital Management with participation by Black Opal Ventures. Empatica intends to use the financing to expand its suite of digital biomarkers for use in both patient care and in clinical trials as digital endpoints. Cris De Luca, partner at Sanofi Ventures and newly-appointed board member at Empatica, said: "By gaining higher resolution into disease symptomology through novel digital measures and digital biomarkers in clinical and real-world settings, Empatica is unlocking the possibilities of early disease detection, enhanced treatment decisions, and improving quality of life for patients around the world." International IT services company, Netcompany, alongside the Department for Health and Social Care (DHSC) have won the Emerging Technology of the Year award in the Technology Excellence category of the 2022 UK IT Industry Awards. The two companies were recognised for their work on the NHS Covid Pass, which also saw them receive a highly commended in the Best Healthcare IT Project of the Year 2022. The win reflects the vital role that the NHS Covid Pass has played in the safe reopening of the country. It allows users to shared their Covid-19 status or vaccination status when travelling internationally. Richard Davies, UK country managing partner at Netcompany, said: "This award recognises our talented teams, expertise, and dedication towards creating technology solutions that help to improve the everyday lives of citizens.

Picture this: As a Covid-19 patient fights for her life on a ventilator, software powered by artificial intelligence analyzes her vital signs and sends her care providers drug-dosing recommendations -- even as the same software simultaneously analyzes in real time the vital signs of thousands of other ventilated patients across the country to learn more about how the dosage affects their care and automatically implements improvements to its drug-dosing algorithm. When an algorithm encounters a real-world clinical setting, adaptive AI might allow it to learn from these new data and incorporate clinician feedback to optimize its performance. Instead of being unleashed, artificial self-control lets a manufacturer put adaptive AI on a longer leash, allowing the algorithm to explore within a defined space to find the optimal operating point. When the algorithm is ready to incorporate what it has learned from real-world data about how drug-dosing information has affected other patients on ventilators, it first goes through a controlled revalidation process, automatically testing its performance on a random sample from a large representative test dataset in the cloud, a dataset that has been carefully curated by the manufacturer to ensure it is representative of the overall population and has high quality information about drug-dosing and patient outcomes. The test is logged, and each data point used in the test is carefully controlled to ensure that the algorithm is not simply getting better and better at predicting the answer in a small test set (a common problem in machine learning called overfitting) but is instead truly improving its performance.

The Israeli medical imaging vendor Nanox says it has a vision for the future of healthcare to address health disparities and lack of access to care. It envisions a new business model and plans to leverage a package of new technologies, including cold-cathode X-ray technology to help reduce costs, coupled with a new and inexpensive imaging system that combines teleradiology with artificial intelligence (AI). The business model is to enable any clinic or hospital in the developing world or rural areas to access its technology and no upfront costs using a pay-per-exam fee. The exams will be read by remote teleradiologists, including subspecialists, and AI will help augment clinical staff and radiologists to offer additional health screenings for all patients scanned. After a few years of talk, the vendor now appears on the edge of making this a reality.

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Artificial Intelligence in the pharmaceutical industry has massive potential to transform drug discovery by accelerating research and timelines to make the drug more affordable and increase the likelihood of FDA approval. It's a powerful data mining tool based on massive pharmacological data and the machine learning process.AI enhances the success rate of the designed drug, helps physicians choose the preferred combination drug, helps consumers select the right medicine, and helps insurers and regulators generate a comprehensive prognosis by analyzing the various data injected by the operator. To explore the AI apps related to pharmaceuticals or the pharm industry, jump into the AI-integrated Blinx AI's AppStore and try the magic of AI https://bit.ly/3hHh4wj

To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural networks. Recently, GraphBP showed its ability to predict innovative "real" chemicals that the binding affinity outperformed with traditional molecular docking methods by using a flow-based generative model with a graph neural network and multilayer perception. However, all those methods regarded proteins as rigid bodies and only include a very small part of proteins related to binding. However, the dynamics of proteins are essential for drug binding. Based on GraphBP, we proposed to generate more solid work derived from protein data bank. The results will be evaluated by validity and binding affinity by using a computational chemistry algorithm.

Is there enough scrutiny of artificial intelligence (AI) software prior to clearance by the Food and Drug Administration (FDA) for adjunctive use in breast cancer screening? Despite the FDA clearance in recent years of several AI products to help identify suspicious breast lesions and facilitate mammography triage, researchers suggested in a recent review, published in JAMA Internal Medicine, that questions remain about data sources, clinical outcome measures and external validation. Here are a few takeaways from their review of the research leading to FDA clearance for nine AI-related products for breast cancer screening between January 1, 2017 and December 31, 2021. All of the clearances for the AI products were based on retrospective analysis of previously existing databases. Only six of the nine products had multicenter studies to support their use and research for four of the AI products lacked information about external validation, according to the review.