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Registration
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Welcome reception
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Displaying
32
contributions
out of
32
AI QA programme: routine, case-specific and ad hoc QC; incidents
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AI in Radiotherapy: (clinical and technical challenges) and applications
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AI in diagnostic radiology and nuclear medicine: (clinical and technical challenges) and applications
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AI terminology and clinical/data science task types for users
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Acceptance and commissioning of imaging-based AI systems
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Bias, subgroup performance and data drift
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CQMP roles across the AI clinical implementation lifecycle
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Clinical evaluation, clinical adoption, impact assessment and decommissioning
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Closing
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Data collection, DICOM, PACS/RIS/TPS and de-identification
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Data curation, labelling, harmonization and bias in ML/DL datasets
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Deep learning basics for imaging-based AI users
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Feature engineering, deep features and foundation models: concepts with clinical examples
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IT infrastructure, cybersecurity and workflow integration
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Introduction to AI, historical background
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Local validation strategies and test datasets, and monitoring?
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Machine learning models and performance metrics
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Participants' presentations
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Participants' presentations: experience, local AI needs and barriers
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Post-course test
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Practical Q&A and consolidation
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Practical exercises - Rotation 1
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Practical exercises - Rotation 2
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Practical exercises - Rotation 3
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Practical exercises - Rotation 4
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Pre-course test
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Procurement, vendor documentation and model cards (assessment of need, pre-selection, demonstration, selection, installation)
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Q&A and consolidation from practicals and lectures so far
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Regulatory, ethical and legal considerations for imaging-based AI
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Roundtable discussion: AI implementation needs, risks and expectations of participants
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Statistical foundations for AI users
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Welcome, introduction and workshop aims
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Organizers
Olivera Ciraj Bjelac (IAEA, Austria), Egor Titovich (IAEA, Austria), Local Organiser: Marco Esposito (ICTP)
Co-sponsors
