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T4. There are numerous resources for AI and informatics from RSNA, SIIM, American College of Radiology, and others. It can be overwhelming, especially when first exploring this domain! Here’s a list of compiled resources to help you on your journey: bit.ly/36QhVFz #RadAIChat

#DeepLearning model highly accurate at classifying cardiac implants chest xrays doi.org/10.1148/ryai.2… Felix Busch Keno Bressem Lisa Adams #CXR #CVRad #DICOM

#DeepLearning model highly accurate at classifying cardiac implants chest xrays doi.org/10.1148/ryai.2… Hugo Aerts Keno Bressem Lisa Adams #ChestRad #AI #MachineLearning

📅 Mark your calendar for the Aug. 7 #RadAIchat at 8 PM ET on "Checklist for #AI in Medical Imaging (CLAIM): 2024 Update" moderated by Mike Klontzas and panelists Lisa Adams Charles Kahn, MD Ali Tejani, MD #radiomics #AI #DL #radres RSNA Hesham Elhalawani

Hi there! I'm Lisa Adams, a physician scientist and radiologist at Technical University Munich. Excited to discuss CLAIM guidelines and their impact on AI reporting in radiology. #RadAIchat

T1. Key AI reporting guidelines in radiology include CLAIM, STARD-AI, MI-CLAIM, CONSORT-AI, SPIRIT-AI, FUTURE-AI, MINIMAR, and RQS. Each addresses specific aspects of AI research reporting and reproducibility. #RadAIchat

T1. The recently updated CLAIM guideline is a comprehensive guideline for AI in medical imaging. It covers 44 items across all manuscript sections, ensuring thorough reporting of data, methods, and results. #RadAIchat

T2. CLAIM guides authors in clear AI research presentation. It covers the entire manuscript structure, ensuring critical details on data, model architecture, training, and evaluation are reported. #RadAIchat

T2. CLAIM serves multiple stakeholders: authors use it for thorough reporting, reviewers for completeness assessment, and readers to evaluate study quality and reproducibility. #RadAIchat

T3. CLAIM 2024 emphasizes precise terminology: ‘internal/external testing’ preferred over ambiguous ‘validation’. Encourages sharing protocols and data/code. Updates aim to standardize AI reporting, increase transparency, and facilitate comparison between studies. #RadAIchat

T3. CLAIM 2024 adds ‘not applicable’ option, adopts ‘reference standard’ instead of ‘ground truth’/’gold standard’, includes image acquisition details. Simplifies by removing data element definitions. Not extended to radiomics research, keeping focus on AI in imaging. #RadAIchat

T4. ‘Ground truth’ implies absolute certainty, while ‘gold standard’ suggests a fixed benchmark. CLAIM 2024 adopts ‘reference standard’ to acknowledge the inherent uncertainty in medical data labeling and align with other reporting guidelines. #RadAIchat

T4. CLAIM 2024 replaces 'ground truth' and 'gold standard' with 'reference standard'. This change acknowledges uncertainty in medical data labeling, aligns with other reporting guidelines like STARD, and avoids implying absolute certainty in benchmarks. #RadAIchat

T5. CLAIM serves as a roadmap for comprehensive AI study reporting in radiology. It guides researchers in study design, helps journals maintain reporting standards, and enables readers to critically assess AI research quality. #RadAIchat

T5. Beyond reporting, CLAIM educates on best practices in AI study design and execution. It can inform curriculum development for AI in radiology and promote standardization across the field, improving research quality and clinical translation. #RadAIchat

An excellent reference point for the key elements of the European Commission's ground breaking #ArtificialInteligence Act and its relevance to healthcare. Don't forget to bookmark this resource to for easy to access references to the relevant chapters. nature.com/articles/s4174…

Our new npj Digital Medicine commentary analyzes the EU AI Act's impact on healthcare. We explain the risk-based approach, explore implications for radiology AI, and discuss regulatory challenges for medical AI. Full article here: rdcu.be/dQDBn Thanks to Felix Busch Keno Bressem

Lisa Adams Keno Bressem discuss the potential clinical applications of biological age from CXRs doi.org/10.1148/ryai.2… #aging #AI #MachineLearning

Is there a future for Meta's open source Llama 3 large language model (LLM) in radiology AI applications? Find out in this original research study by Lisa Adams, Daniel Truhn, Felix Busch, Keno Bressem et al. TU München Charité - Universitätsmedizin Berlin bit.ly/4cpeJwJ

Lisa Adams Keno Bressem discuss prognostic value of CXR-Age model in a large test cohort doi.org/10.1148/ryai.2… #ChestRad #aging #Ageing