Washington: Researchers believe that developing cutting-edge artificial intelligence (AI) that can quickly and accurately identify lung diseases like pneumonia and tuberculosis could relieve the strain that winter months place on hospitals. Tuberculosis and pneumonia - potentially serious infections which mainly affect the lungs -often require a combination of different diagnostic tests,- such as CT scans, blood tests, X-rays, and ultrasounds. These tests can be expensive, with often lengthy waiting times for results. Developed by UWS, the revolutionary technology - originally created to quickly detect Covid-19 from X-ray images - has been proven to automatically identify a range of different lung diseases in a matter of minutes, with around 98 per cent accuracy. UWS researcher Professor Naeem Ramzan said: "Systems such as this could prove to be crucial for busy medical teams worldwide."

Bethesda (Maryland) [US], April 16 (ANI): A new study has found that a fully-automated artificial intelligence (AI) deep learning model can identify early signs of type 2 diabetes on abdominal CT scans. The findings of the study were published in the journal, 'Radiology'. Type 2 diabetes affects approximately 13 per cent of all U.S. adults and an additional 34.5 per cent of adults meet the criteria for pre-diabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of pre-diabetes can last up to 8 years and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease.

Digital technologies and artificial intelligence (AI) in particular have tremendous potential to deliver the energy sector's climate goals more rapidly and at lower cost, according to a new study. As the impacts of climate change become more visible worldwide, governments and industry face the urgent challenge of transitioning to a low-carbon global energy system. The World Economic Forum's report highlights the technologies' potential to support the energy transition and advances a set of principles for the energy industry to deploy AI in a "safe, fair, and trustworthy way". Produced in collaboration with BloombergNEF (BNEF) and German energy agency Deutsche Energie-Agentur (Dena), Harnessing AI to accelerate the energy transition reviews the state of play of AI adoption in the energy sector. It also identifies high-priority applications of AI in the energy transition and offers a road map and practical recommendations for the energy and AI industries to maximise AI's benefits.