With artificial intelligence making its way into daily life, healthcare, including ophthalmology, is no exception. Ophthalmology, with its heavy reliance on imaging, is an innovator in the field of AI in medicine. Although the opportunities for patients and health care professionals are great, hurdles to fully integrating AI remain, including economic, ethical, and data-privacy issues. "AI is impacting health care at every level, from the provider to the payer to pharma," according to Dan Riskin, MD, CEO and founder of Verantos, a health care data company in Palo Alto, California, that uses AI to sort through real world evidence. The question remains, just how to patients feel about the use of AI in the diagnosis and treatment of their illnesses? In a patient survey conducted in December 2019, 66% of respondents said AI plays a large role in their diagnosis and treatment and thought it was important.
A recent study has used machine learning analysis techniques to chart the readability, usefulness, length and complexity of more than 50,000 privacy policies on popular websites in a period covering 25 years from 1996 to 2021. The research concludes that the average reader would need to devote 400 hours of'annual reading time' (more than an hour a day) in order to penetrate the growing word counts, obfuscating language and vague language use that characterize the modern privacy policies of some of the most-frequented websites. 'The average policy length has almost doubled in the last ten years, with 2159 words in March 2011 and 4191 words in March 2021, and almost quadrupled since 2000 (1146 words).' The mean word count and sentence count among the corpus studied, over a 25 year period. Though the rate of increase in length spiked when the GDPR and the California Consumer Privacy Act (CCPA) protections came into force, the paper discounts these variations as'small effect sizes' which appear to be insignificant against the broader long-term trend.
Cardiovascular Disease has long been the number one cause of death in the U.S. and some of the stats are startling: an American will have a heart attack approximately every 40 seconds for a total of 805,000 every year, At the same time, mortality and morbidity rates of CVD are increasing year by year, especially in developing regions. Studies have shown that approximately 80% of CVD-related deaths occur in low- and middle-income countries. Besides, these deaths occur at a younger age than in high-income countries. CVD represents a significant economic cost for society, around $351.2 billion in the US, chronically affecting patients' quality of life. The EU has estimated that the overall yearly cost amounts to €210 billion, allocating around 53% to healthcare costs (€111 billion), with 26% related to productivity losses (€54 billion), and the remaining 21% (€45 billion) to the informal care of people with CVD (European Cardiovascular Disease Statistics 2017).
Toyota Motor North America (TMNA) is partnering with Invisible AI to deploy artificial intelligence (AI) in its factories to enhance efficiency and safety. The computer vision platform of the Texas-based company will be installed in 14 Toyota factories in North America. The AI will analyze manufacturing operations to detect any technical issues, revealing the invisible problems to the human eye and cameras and fixing them to improve processes' quality and safety. According to Forbes, Toyota aims to apply computer vision technology to accurately review the assembly process and reduce the time to find inefficiencies. Under the two-year agreement, Toyota factories will be equipped with a system consisting of 500 AI devices using NVIDIA processors and a high-resolution 3D camera to observe operations.
As we know that the global lockdown has hyped up a digital presence. Videotapes have reached new heights. Videotape games have kept the users hooked so hard on the digital world that this has become one of the strongest digital marketing strategies. How? Videotape gamings have come up with a creation - Digital Conflagrations. What happens here is that communities such as the gamers, e-sports enthusiasts gather to play/interact.
Chintan Shah is a senior product manager at NVIDIA, focusing on AI products for intelligent video analytics. Chintan manages an end-to-end toolkit for efficient deep learning training and real-time inference. Previously, he developed hardware IPs for NVIDIA GPUs. Chintan holds a master's degree in electrical engineering from North Carolina State University.
People with disabilities often cannot count on modern digital devices, software, and services to be accessible. Will streaming video platforms include closed captions for viewers who are deaf or hard of hearing? How will virtual assistants work for users with speech disabilities? Can websites be read aloud by text-to-speech engines for readers who are blind or visually impaired? How will smartphones be accessed by people with physical and mobility disabilities?
With the right technology solutions, companies can aim to relieve rising levels of burnout among health care workers. More than two years into the pandemic, depleted health care workers have been pushed to their limits. In the U.S., we're experiencing what Becker's Hospital Review has described as "an unprecedented nursing shortage." Overworked and risking their own health -- both physical and mental -- to provide care throughout multiple surges of COVID-19, nurses are in crisis. Many are leaving the profession -- and the problem is global.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. As AI adoption continues to ramp up exponentially, so is the discussion around -- and concern for -- accountable AI. While tech leaders and field researchers understand the importance of developing AI that is ethical, safe and inclusive, they still grapple with issues around regulatory frameworks and concepts of "ethics washing" or "ethics shirking" that diminish accountability. Perhaps most importantly, the concept is not yet clearly defined. While many sets of suggested guidelines and tools exist -- from the U.S. National Institute of Standards and Technology's Artificial Intelligence Risk Management Framework to the European Commission's Expert Group on AI, for example -- they are not cohesive and are very often vague and overly complex.