Aidoc, the leading provider of healthcare AI solutions, today announced that it received FDA 510(k) clearance for its triage and notification of pneumothorax on X-ray exams. A one-stop partner for the enterprise's clinical AI needs, Aidoc's other seven FDA-cleared solutions are already implemented across U.S. health systems, flagging and communicating suspected pathologies in CT exams – and now have expanded to the high volume X-ray modality. Aidoc's newly FDA-cleared solution runs on all X-ray machines including portable ones, and is designed to analyze X-ray images. It automatically flags positive cases of pneumothorax, facilitating physicians to read X-rays in a timely manner. The ability to quickly identify pneumothorax is imperative as it can worsen rapidly and result in respiratory or cardiac failure.

One down, one to go. After locking down its first FDA clearance, Wision A.I. is already lining up its next regulatory score.

Did you miss a session at the Data Summit? As the world becomes increasingly dependent on technology to communicate, attend school, do our work, buy groceries and more, artificial intelligence (AI) and machine learning (ML) play a bigger role in our lives. Living through the second year of the COVID-19 pandemic has shown the value of technology and AI. It has also revealed a dangerous side and regulators have responded accordingly. In 2021, across the world, governing bodies have been working to regulate how AI and ML systems are used.

Earlier this year, the Food and Drug Administration (FDA) released the Artificial Intelligence/Machine Learning-Based Software as a Medical Device ( …

After initial release of a machine learning algorithm, the model can be fine-tuned by retraining on subsequently gathered data, adding newly discovered features, or more. Each modification introduces a risk of deteriorating performance and must be validated on a test dataset. It may not always be practical to assemble a new dataset for testing each modification, especially when most modifications are minor or are implemented in rapid succession. Recent works have shown how one can repeatedly test modifications on the same dataset and protect against overfitting by (i) discretizing test results along a grid and (ii) applying a Bonferroni correction to adjust for the total number of modifications considered by an adaptive developer. However, the standard Bonferroni correction is overly conservative when most modifications are beneficial and/or highly correlated. This work investigates more powerful approaches using alpha-recycling and sequentially-rejective graphical procedures (SRGPs). We introduce novel extensions that account for correlation between adaptively chosen algorithmic modifications. In empirical analyses, the SRGPs control the error rate of approving unacceptable modifications and approve a substantially higher number of beneficial modifications than previous approaches.

Therapy has an engagement problem. Despite the benefits of treatment plans and at-home exercises, people generally resist anything that feels like work, and this impedes the mental-health recovery process across the board. Clinicians have attempted to bridge this gap with various devices and reward systems, but still, it's often incredibly difficult to motivate patients to help themselves. Video games have the opposite problem. Players can spend hours immersed in a single digital experience, seated in one spot and lost in their own world, but they're often branded as "lazy" for this behavior.

This is the web version of dot.LA's daily newsletter. Sign up to get the latest news on Southern California's tech, startup and venture capital scene. Earlier this week, West Hollywood-based startup Pearl announced that its Second Opinion product had become the first AI-enabled device cleared by the Food and Drug Administration to read dental x-rays. Using the power of artificial intelligence, Second Opinion is meant to help dentists find maladies they'd otherwise miss through the eye test. Getting FDA clearance is not easy, especially because Pearl had to prove its device could detect a variety of dental conditions (most medical devices have to prove only one capability).

There are now some 120,000 warehouses globally, and another 50,000 are likely to be added before 2025. Over the next few years, more robots will be deployed into these warehouses--the logistics market--than in all other application categories combined, including farming, medicine, and home use. Just as the 1960s saw the mechanization of industry, with an accompanying boom in productivity and prosperity, the 2020s will be the dawn of the robotification of services. Industrial robots came into use in 1961 when General Motors (G.M.) installed a simple robotic arm on its New Jersey production line. The machine had been invented by Unimation, a company founded by the father of robotics, Joseph Engelberger--a self-professed Isaac Asimov enthusiast.

An emerging artificial intelligence (AI) software that reportedly reduces false negative rates for fractures by 29 percent has received FDA clearance. BoneView AI (Gleamer) detects fractures on X-rays, highlights regions of interest and submits them to radiologists for confirmation, according to the French company Gleamer. The company said the algorithm was designed to aid a variety of physicians who read X-rays in clinical practice. Noting that traumatic injuries account for one-third of visits to emergency rooms (ERs), Gleamer noted that errors with fracture interpretation, which are common during evening hours, can represent up to 24 percent of harmful diagnostic errors in the ER. The company said these errors may result from fatigue and non-expert reading of X-rays.

Original article by Azfar Adib, who is currently pursuing his PhD in Electrical and Computer Engineering in Concordia University in Montreal. He is a Senior Member in the Institute of Electrical and Electronic Engineers (IEEE). A recent announcement by Meta about terminating the face recognition system in Facebook sparked worldwide attention. It comes as a sort of new reality for many Facebook users, who have been habituated for years to the automatic people recognition feature in Facebook photos and videos. Since the arrival of mankind on earth, facial outlook has remained as the most common identifier for humans.