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T4. Fine-tuning is required if you want to use BERT for specific labels. That is, before using BERT, you must label some data and train the model. #RadAIchat

T4. BERT models can help classify text for common diseases, but will struggle with edge cases. #RadAIChat

T4. Keeping BERT models up to date with the latest clinical guidelines and research requires ongoing maintenance and retraining. #RadAIChat

T5. A great resource for learning about BERT and AI in general is fast.ai by Jeremy Howard. I highly recommend it to anyone starting out in AI (course.fast.ai). #RadAIchat

T5. If you want to understand the transformer architecture better, I recommend "The Illustrated Transformer" by Jay Alammar. jalammar.github.io/illustrated-tr… #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

Felix Busch Hugo Aerts Keno Bressem Lisa Adams present a #DeepLearning model to classify implants on chest xrays doi.org/10.1148/ryai.2… Heart_SCCT #SCCT2024 #CVRad

New article! Read Navigating the European Union Artificial Intelligence Act for Healthcare dlvr.it/TBpgMn in npj Digital Med

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 too for easy to access references to the relevant chapters. nature.com/articles/s4174…

Chest Radiographs as Biological Clocks: Implications for Risk Stratification and Personalized Care doi.org/10.1148/ryai.2… Lisa Adams Keno Bressem #DeepLearning #AI #MachineLearning

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

Exciting news! 🚀 We've just launched our innovative solution to one of the biggest #challenges in medical research: extracting critical information from free-text data using our #LLM-based pipeline! Jakob Nikolas Kather Dyke Ferber @prof_ebert Keno Bressem Heiko Böhme medrxiv.org/content/10.110…

How will large language models transform structured reporting in radiology and beyond? In our comprehensive review just published in European Radiology, we deeply dive into the past, present, and future of LLMs for radiology reporting. Read the article here: doi.org/10.1007/s00330…

🛡️Privacy-preserving, open-source large language models show promise as an alternative to OpenAI’s GPT-4 for accurate chest radiograph report classification. Exciting potential for safer AI in healthcare! Felix Dorfner Keno Bressem QTIM Lab #AI #Radiology pubs.rsna.org/doi/10.1148/ra…

Happy International Day of Radiology!🌐 Radiology has come far since the discovery of X-rays in 1895. Today, MRIs, ultrasound and AI-driven tools aid in diagnosis and treatment. In 2023 alone, the FDA cleared 765+ AI healthcare algorithms in radiology! #IDoR2024 #Radiology #AI