A diverse writing group—lead by authors at the University of Toronto—has developed an approach for radiology departments and practices to reduce their greenhouse gas emissions and become more resilient to the effects of climate change. Kate Hanneman bit.ly/4b90AmW

🚨 Hot off the press! I am incredibly grateful for the opportunity to share my thoughts on the potential and challenges of #GenAI research in #radiology. Special 🙏 to Linda Moy for her guidance in my first Radiology editorial. #radtwitter Rad AI buff.ly/3WLD5fX

Many thanks to our dedicated authors, reviewers, editorial board, and publications team

In this week's @RadiologyEditor choice article, Wei and Song et al construct a deep learning model and measure its accuracy for segmentation and detection of cerebral aneurysms at CT angiography. bit.ly/3XcFvUP

Perfect weekend listening! 🎧 Check out the latest Radiology podcast where Refky Nicola, M.S. D.O. and Dr. Amy Young explore the impact of positive leadership on burnout and engagement. #RadPodcasts

🚨Kick off your weekend with this week's Radiology podcast episode!🚨 Reni Butler discusses how patient characteristics impact AI performance on negative screen digital breast tomosynthesis with Derek Nguyen, MD, Yinhao Ren, and Lars J Grimm. Don’t miss it! 🎧 #RadPodcasts

#RadAIchat  Hey Linda Moy here, Editor of Radiology @RadiologyEditor Linda Moy

Stay up to date on the U.S. FDA Mammography Quality Standards Act (MQSA) amendments, which go into effect this month. These amendments cover reporting of breast tissue density, handling of patient records, and more. bit.ly/3MuQaUu

Curious about the impact podcasts have on the amplification of radiology journal articles? Check out this study by Wang et al comparing download and citation metrics of the podcasted and non-podcasted articles in RSNA Radiology. USask Radiology Research bit.ly/3ZbTJGF

#RadAIchat Hi, I’m Linda Moy, Editor of Radiology @RadiologyEditor Linda Moy

#RadAIchat T1 Reporting guidelines promote clear reporting of methods and results to allow critical evaluation of a paper

#RadAIchat Let's learn about CLAIM

T2. Not all reporting guidelines are suitable for AI research in medical imaging #AI specific challenges require tailored guidelines, such as #CLAIM to ensure accurate reporting #RadAIchat doi.org/10.1148/ryai.2…

T2. Only some guidelines are suitable for AI research in medical imaging #RadAIchat

T2. Really great table that summarizes various guidelines Mike Klontzas #RadAIchat

T3. TRIPOD+AI is a checklist tailored to developing or validating a prediction model #RadAIchat

T3. #CLAIM focuses on #AI research in all types of medical imaging #radiology #pathology #opthomalogy #RadAIchat

T4. CLAIM also focuses on data curation, reference standard, model training, evaluation and performance #AImodels #RadAIchat

T5. No, CLAIM is not suitable for radiomics research #RadAIchat

T5. The update panel members agreed that the checklist should not be extended to research on imaging biomarkers #RadAIchat