The integration of AI tools into medical applications has aimed to improve the efficiency of diagnosis. The emergence of large language models (LLMs), such as ChatGPT and Claude, has expanded this integration even further. Because of LLM versatility and ease of use through APIs, these larger models are often utilised even though smaller, custom models can be used instead. In this paper, LLMs and small discriminative models are integrated into a Mendix application to detect Covid-19 in chest X-rays. These discriminative models are also used to provide knowledge bases for LLMs to improve accuracy. This provides a benchmark study of 14 different model configurations for comparison of accuracy and environmental impact. The findings indicated that while smaller models reduced the carbon footprint of the application, the output was biased towards a positive diagnosis and the output probabilities were lacking confidence. Meanwhile, restricting LLMs to only give probabilistic output caused poor performance in both accuracy and carbon footprint, demonstrating the risk of using LLMs as a universal AI solution. While using the smaller LLM GPT-4.1-Nano reduced the carbon footprint by 94.2% compared to the larger models, this was still disproportionate to the discriminative models; the most efficient solution was the Covid-Net model. Although it had a larger carbon footprint than other small models, its carbon footprint was 99.9% less than when using GPT-4.5-Preview, whilst achieving an accuracy of 95.5%, the highest of all models examined. This paper contributes to knowledge by comparing generative and discriminative models in Covid-19 detection as well as highlighting the environmental risk of using generative tools for classification tasks.

Effective and reliable screening of patients via Computer-Aided Diagnosis can play a crucial part in the battle against COVID-19. Most of the existing works focus on developing sophisticated methods yielding high detection performance, yet not addressing the issue of predictive uncertainty. In this work, we introduce uncertainty estimation to detect confusing cases for expert referral to address the unreliability of state-of-the-art (SOTA) DNNs on COVID-19 detection. To the best of our knowledge, we are the first to address this issue on the COVID-19 detection problem. In this work, we investigate a number of SOTA uncertainty estimation methods on publicly available COVID dataset and present our experimental findings. In collaboration with medical professionals, we further validate the results to ensure the viability of the best performing method in clinical practice.

Modzy, the leading enterprise AI platform, announced that DarwinAI is now a model partner at the Modzy AI Model Marketplace. DarwinAI is expected to deploy numerous models using its GenSynth platform, including COVID-Net, an open source deep neural network for detecting COVID-19 infections from chest X-rays. DarwinAI has been featured in the MIT Technology Review, AI in Healthcare, and VentureBeat for their innovations in combating COVID-19 using AI. "We're really excited for this partnership with DarwinAI," said Norm Litterini, Head of Partnerships at Modzy. "The quality of their work is reflected in their solid reputation and industry acknowledgment. DarwinAI's models will enable customers to quickly operationalize AI into strategic initiatives while building out our marketplace offering, particularly for biomedical applications, where there is critical need."

As the number of COVID-19 infections are again spiking around the U.S., health care workers struggling to stay ahead have a tool with a novel approach to add to their arsenal in COVID-Net, an open source AI-based platform that uses radiological lung images to determine COVID-19-specific lung damage, as well as assess the degree of that damage. The technology was developed in March, during the early days of the pandemic, but has been gaining more notice as an example of artificial intelligence in health care as more organizations have adopted it. Although the nonprofit project is being led by Red Hat, Boston Children's Hospital and DarwinAI (a 3-year-old proprietary artificial intelligence startup headquartered in Waterloo, Ontario), it began as a collaboration between Canada's University of Waterloo and DarwinAI. "COVID-Net was an initiative to try to contribute to the whirlwind of the pandemic in March," DarwinAI CEO Sheldon Fernandez told ITPro Today. "We open sourced it and we didn't want it to be commercial.

RALEIGH – Red Hat is not known necessarily as a life science firm, but the open source technology giant is going after COVID-19 as part of two new efforts to combat the global scourge. "Working with Red Hat Open Innovation Labs provided a more flexible and responsive approach for creating solutions using open source technologies. We were able to build a DevOps platform that can not only deliver relevant, timely COVID-related information and knowledge to health workers globally, but one that can also scale and adapt to their future needs." A WHO team spent an eight-week virtual residency with Red Hat Open Innovation Labs "to help organizations integrate people, practices and technology to increase agility in the development of software and products, catalyze innovation and solve internal challenges in an accelerated time frame," Red Hat explained. On the AI front, Red Hat is partnering with DarwinAI, the University of Waterloo and Boston Children's, a pediatric hospital, to advance a project called COVID-Net.

DarwinAI, the explainable artificial intelligence (XAI) company, and Red Hat, the world's leading provider of open source solutions, announced a collaboration to accelerate the deployment of COVID-Net--a suite of deep neural networks for COVID-19 detection and risk stratification via chest radiography--to hospitals and other healthcare facilities. DarwinAI and Red Hat are also leveraging the expertise of a computation research group, the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children's Hospital to better focus the software for real world clinical and research use. "The COVID-Net system is a promising tool, but needs to be coupled with a compelling GUI to be effective -- Boston Children's ChRIS framework and the Red Hat OpenShift platform provides an effective way to get COVID-Net into the hands of health care professionals on the front lines." Since the launch of COVID-Net by DarwinAI and the University of Waterloo's Vision and Imaging Processing (VIP) Lab, the project has continued to evolve with assistance, participation and collaboration from researchers and clinicians around the world. The initiative eventually led to a collaboration between DarwinAI and Red Hat, using underlying technology from Boston Children's, the number one pediatric hospital in the nation.

An enemy with which we are befuddled. And unless you were living under a rock for the past couple of months(like Jared Leto), you know what I'm talking about – COVID-19. Whether you turn on the news, or scroll through social media, the majority of information that you take in nowadays is about the SARS-COV2 virus, or the Novel Corona Virus. But among all the negativity, there was a sliver of light shining through. When faced with a common enemy, mankind united across borders(for the most part; there are bad apples always) to help each other tide over the current assault. Scientists, who are the heroes of the day, doubled down to find a cure, vaccine, and a million other things which helps in the battle against COVID-19.

The news: An open-access neural network called COVID-Net, released to the public this week, could help researchers around the world in a joint effort to develop an AI tool that can test people for Covid-19. You can read all of our coverage of the coronavirus/Covid-19 outbreak for free, and also sign up for our coronavirus newsletter. But please consider subscribing to support our nonprofit journalism.. COVID-Net is a convolutional neural network, a type of AI that is particularly good at recognizing images. Developed by Linda Wang and Alexander Wong at the University of Waterloo and the AI firm DarwinAI in Canada, COVID-Net was trained to identify signs of Covid-19 in chest x-rays using 5,941 images taken from 2,839 patients with various lung conditions, including bacterial infections, non-Covid viral infections, and Covid-19. The data set is being provided alongside the tool so that researchers--or anyone who wants to tinker--can explore and tweak it. Don't believe the hype: Several research teams have announced AI tools that can diagnose Covid-19 from x-rays in the last few weeks.

On Dec. 31, BlueDot, a Toronto-based company that uses artificial intelligence to track the spread of infectious diseases, alerted its customers about a cluster of unusual pneumonia cases in Wuhan, China. Nine days later, the World Health Organization confirmed the discovery of a novel coronavirus, later named COVID-19, in Wuhan. Today, COVID-19 is a pandemic that has spread to 180 countries, claimed more than 83,000 lives, and triggered a near-global lockdown. And for the moment, the best solution to contain the spread of the virus is to improve personal hygiene and exercise social distancing. In the meantime, politicians, scientists, and researchers are teaming up to find systematic ways to fight the virus and care for patients.

Find a hospital taking in coronavirus cases, and you'll most likely find departments often in need of more staff and without enough testing kits. Now one Canadian AI startup is hoping to develop tools that will automatically detect COVID-19 infections from X-rays, and help guide medical professionals on how seriously the infection has taken hold. DarwinAI, which spun out of work at the University of Waterloo, normally works on AI explainability. The company makes a tool that can show why deep-learning modules make the decisions they do, enabling users to correct the inputs that lead to wrong decisions, and fix the architecture or retrain the system to prevent the same mistakes in future. The idea is that, by getting an insight into why AI does what it does, companies can speed up the development of their AI products.