Demand is rising for AI tools in retail. Read the Gartner® Report, “Emerging Technologies and Trends Impact Radar: Artificial Intelligence in Retail” now. Learn More
Demand is rising for AI tools in retail. Read the Gartner® Report, “Emerging Technologies and Trends Impact Radar: Artificial Intelligence in Retail” now. Learn More
Detecting if employees are following proper handwashing steps is important in upholding high sanitation standards and avoiding the contamination of surfaces through touch. As part of our computer vision for healthcare lineup, Chooch AI’s handwashing detection AI models ensures compliance with hygiene standards and lowers the risk of spreading diseases, meaning fewer employees out sick and less downtime on important projects.
Speed, safety, and accuracy are crucial in the healthcare industry. Computer vision in healthcare applications are revolutionizing the healthcare industry by making it easier for healthcare organizations to improve patient care and streamline their internal processes.
Healthcare facilities throughout the world are suffering from critical staff shortages, and the COVID-19 pandemic has only made the situation worse. According to November 2020 statistics from the U.S. Department of Health and Human Services, 18% of U.S. hospitals said that they were critically short on medical staff. Patient monitoring AI can dramatically improve the ability of hospitals and medical facilities to monitor situations.
The speed of technological innovation in the healthcare industry has been moving at a breakneck pace for decades. Now, artificial intelligence – and computer vision solutions in particular – are now poised to deliver increasingly powerful benefits to patients and providers. In this webinar you will learn about a wide range of solutions including cell identification, procedure tracking, gesture analysis, safety and security.
Chooch AI models have been developed and deployed for a growing number of applications and demos are available for these healthcare applications. Now, we’ll be demoing a wide variety of applications in healthcare at HIMSS 2021 in the startup area C100-78. Please contact us to meet. We also contribute a blog post to HIMSS about the value of computer vision in healthcare and did a healthcare podcast with HIMSS.
Counting and identifying cells is a tedious and time-consuming process. In many cases, highly paid Ph.D. scientists perform these tasks in the fields of histology, immunology, oncology, and pharmaceutical research. Unfortunately, the painstaking process involves long hours of looking at samples under a microscope and manually counting each cell – even worse, traditional cell counting methods leave a lot to be desired in terms of accuracy.
Mary Sheridan, Senior Manager for the Accelerate Health team here at HIMSS interviews Chooch AI CEO Emrah Gultekin about the work Chooch AI is doing with computer vision for healthcare . In this 15 minute podcast Gultekin explains that “in healthcare, you have lots of visual tasks. Whether it’s cell counting or whether it’s patient gestures, or maybe operating rooms where actions are happening. These are the kinds of things that we have used Chooch for and is being used in production in many, many different healthcare scenarios.”
As computer vision is becoming increasingly sophisticated, it brings business benefits to a wide variety of industries. From defect detection to loss prevention, computer vision is a powerful tool with the potential to improve processes and results in many contexts. But before we dive into the use cases of computer vision, let’s define what it is.
Facial recognition is often seen in a bad light due to our fear of Orwellian surveillance states. But facial recognition – when used for the public good especially in the form of facial authentication – can bring numerous benefits. Because while facial recognition certainly can be employed in some shady ways, facial authentication actually increases individual security rather than decreases it.
Over the past decade, computers using deep neural networks have been able to approach—if not exceed—human performance at object recognition tasks. In 2015, the PReLU-Net deep network became the first computer model to surpass human accuracy on the ImageNet 2012 dataset, with 4.94 percent error compared with humans’ 5.1 percent.