We are in a grave situation where our healthcare infrastructure is under intense pain. We have tried all means to tackle the growing needs of the people but the available infrastructure is not good enough for the same. In these limited resources what can help us is the right kind of "Automation". Automation not only in the process but also in the technology which handles patients. Product Brief: Best Kiosk provides a self-service channel for patients to register, check in for consultations, book appointments, and make payments.
In a development expected to help diagnose Covid-19 in suspected patients faster, the Defence Research and Development Organisation (DRDO) and Centre for Artificial Intelligence and Robotics (CAIR) have created an artificial intelligence (AI) algorithm to help detect Covid-19 from chest X-rays. According to its developers, the tool named Atman AI used for Chest X-ray screening has shown an accuracy rate of 96.73 percent. Dr. U K Singh, Director, CAIR, DRDO said the development of the diagnostic tool was part of DRDO's effort to help clinicians and partners on the frontline to help rapidly diagnose and effectively treat COVID-19 patients. "Given the limited testing facilities for coronavirus, there is a rush to develop AI tools for quick analysis using X-rays. The tool will help in automatically detecting radiological findings indicative of Covid-19 in seconds, enabling physicians and radiologists to more effectively triage the cases, especially in an emergency environment," he explained.
Artificial intelligence, the technology that is seen as a home name today is poised to become a transformational force in healthcare. Healthcare industry is where a lot of challenges are encountered and opportunities open up. Starting from chronic diseases and radiology to cancer and risk assessment, artificial intelligence has shown its power by deploying precise, efficient, and impactful interventions at exactly the right moment in a patient's care. The complexity and rise of data in healthcare have unveiled several types of artificial intelligence. Today, artificial intelligence and robotics have evolved to the stage where they can take better care of patients better than medical staff and human caretakers.
The Defence Research and Development Organisation (DRDO) has developed an artificial intelligence algorithm that can detect the presence of the COVID-19 virus in chest X-rays. The AI tool, ATMAN AI, was developed by DRDO's Centre for Artificial Intelligence and Robotics (CAIR), with support from 5C Network & HCG Academics. Triaging using X-ray in COVID-19 diagnosis is a method for the rapid identification and assessment of the lungs, according to a statement issued by HCG Academics. The tool will be used by 5C Network, the country's largest digital network of radiologists, with the support of HCG Academics. Triaging potential patients using X-ray is fast, cost-effective, and efficient.
Bangalore: DRDO Centre for Artificial Intelligence and Robotics on Friday announced that it has developed an Artificial Intelligence algorithm, "ATMAN AI" for Chest X Rays screening to detect Covid-19, in collaboration with 5C Network & HCG Academics. This new AI tool will be used by 5C Network, India's largest digital network of Radiologists, with support of HCG Academics across India. The product has been designed to reduce the burden of CT scans and make covid diagnosis accessible for smaller towns by helping in making the process of X-ray screening fast and efficient. Mr. Kalyan Sivasailam, CEO of 5C network, commented, "Utilizing the algorithms for chest X-ray is an effective triaging tool which can be accessible to the common man in remotest districts of this country. This will have a significant impact on timely care and appropriate treatment."
In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine learning methods LIME and SHAP and the alternative explanation approach CIU, centered on the Contextual Value and Utility (CIU). The produced explanations were evaluated using human evaluation.
Cloud-based medical image management company Ambra Health announced Tuesday it will partner with the vendor neutral artificial intelligence (AI) platform Arterys. It's a move that will streamline interoperability and accelerate the use of AI applications, the companies said. "We're making AI real by improving the physician experience," said John Axerio-Cilies, chief executive officer of Arterys. "We are increasing diagnosis, treatment accuracy, and ultimately outcomes that matter to patients and providers." This partnership brings together Arterys' seven AI solutions that have been cleared by the U.S. Food & Drug Administration, including Cardio AI, Lung AI, and Neuro AI, with Ambra's interoperable, customizable cloud platform that consolidates multiple imaging systems that allows for secure access to imaging data anywhere, anytime.
People in the U.S. are waiting longer to have babies. And more and more families are seeking help with getting pregnant. In fact, according to Penn Medicine, one million babies have been born between 1987 and 2015 using in vitro fertilization or other assisted technology. But IVF success rates remain relatively low. There is a 21.3% chance of full-term normal birth weight and singleton live birth per assisted reproductive technology cycle, Penn Medicine states.
From life-changing implementations like medical diagnostics imaging and self-driving vehicles to humble use cases such as virtual assistants or robot vacuums -- artificial intelligence is being put to use to solve an incredible range of problems. Data is one of the most important elements in developing an AI algorithm. Remember that just because data is being generated faster than ever before doesn't mean the right data is easy to come by. Low-quality, biased, or incorrectly annotated data can (at best) add another step. These extra steps will slow you down because the data science and development teams must work through these on the way to a functional application.