@LCAdamsRad profile picture
Lisa Adams @LCAdamsRad

Radiology MD & Assistant Professor @TU_Muenchen | Former Postdoctoral Fellow @StanfordMedicine | Focused on #Radiology, #AI, #MolecularImaging

Joined April 2023
  • Tweets 47
  • Following 117
  • Followers 169
4 Photos and videos
Lisa Adams retweeted
Radiology: Artificial Intelligence@Radiology_AI
20 Aug 2024
Replying to @LCAdamsRad
@LCAdamsRad @k_bressem discuss prognostic value of CXR-Age model in a large test cohort doi.org/10.1148/ryai.240410 #ChestRad #aging #Ageing
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Lisa Adams retweeted
Radiology@radiology_rsna
14 Aug 2024
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 @LCAdamsRad, @DanielTruhn, @Fel_Busch, @k_bressem et al. @TU_Muenchen @ChariteBerlin bit.ly/4cpeJwJ
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Lisa Adams retweeted
Radiology: Artificial Intelligence@Radiology_AI
14 Aug 2024
Replying to @LCAdamsRad
@LCAdamsRad @k_bressem discuss the potential clinical applications of biological age from CXRs doi.org/10.1148/ryai.240410 #aging #AI #MachineLearning
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Lisa Adams@LCAdamsRad
13 Aug 2024
Our new @npjDigitalMed 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 @Fel_Busch @k_bressem @DanielTruhn @christianjohner @jnkath
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Lisa Adams@LCAdamsRad
12 Aug 2024
RT @npjDigitalMed: An excellent reference point for the key elements of the @EU_Commission's ground breaking #ArtificialInteligence Act and…
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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
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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
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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
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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
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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
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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
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T2. CLAIM serves multiple stakeholders: authors use it for thorough reporting, reviewers for completeness assessment, and readers to evaluate study quality and reproducibility. #RadAIchat
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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
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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
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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
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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
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Lisa Adams retweeted
Radiology: Artificial Intelligence@Radiology_AI
6 Aug 2024
📅 Mark your calendar for the Aug. 7 #RadAIchat at 8 PM ET on "Checklist for #AI in Medical Imaging (CLAIM): 2024 Update" moderated by @Klonmich and panelists @LCAdamsRad @cekahn @AliTejaniMD #radiomics #AI #DL #radres @RSNA @HElhalawaniMD
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Lisa Adams retweeted
Radiology: Artificial Intelligence@Radiology_AI
24 Jul 2024
#DeepLearning model highly accurate at classifying cardiac implants chest xrays doi.org/10.1148/ryai.230502 @HugoAerts @k_bressem @LCAdamsRad #ChestRad #AI #MachineLearning
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Lisa Adams retweeted
Radiology: Artificial Intelligence@Radiology_AI
17 Jul 2024
#DeepLearning model highly accurate at classifying cardiac implants chest xrays doi.org/10.1148/ryai.230502 @Fel_Busch @k_bressem @LCAdamsRad #CXR #CVRad #DICOM
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T5. Hugging Face hosts thousands of BERT models for various tasks, including medical applications. It offers tutorials and tools for using these models in radiology and healthcare, making it a valuable resource for integrating AI into clinical practice. #RadAIchat
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