What Is Clinical Data Informatics?
Clinical Data Informatics (CDI) encompasses AI, systems engineering, MLOps, design, building, and monitoring medical imaging in a more sophisticated data management infrastructure.
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Clinical Practice
AI and Computer Vision Machine Learning (CVML) on medical images already show potential to make predictions from medical images that are clinically valuable. To get the most value out of clinically-oriented CVML requires a deep understanding of medical imaging technology and clinical fields that produce medical images. Similarly, understanding and evaluating the value of CVML products requires understanding both clinical and business effects of the predictions made by these products.
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What Is Pathology?
Gain an overview of the types of Pathology images, what pathologists do, and how image formats relate to DICOM Whole…
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Business Intelligence and AI Monitoring via IHE SOLE
Pursue understanding the IHE Standardized Operational Log of Events (IHE SOLE). Visualize the effects of AI in your department using…
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Deep Dive into Radiology Exam Creation
Deep dive into radiology exam creation alongside CDI faculty Ameena Elahi, MPA, RT(R), CIIP in the modern clinical workspace, discovering…
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What Is A Radiologist?
Join Benjamin Mervak, MD as he talks about the role of the radiologist and how they may generate medical data.
Medical Imaging Data
There is no good AI without good data. Medical images are both vitally valuable and have unique characteristics, some of which are substantially different from other data. How to think about medical imaging data, how to manage it, and what medical image data mean from business and clinical standpoints have begun a breathtaking paradigm shift. Medical imaging assets, processes, and infrastructure are critical to an organization’s long-term successful use of AI.
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EHR Data, Formats, & Data Warehousing
Healthcare Data comes from many clinical specialties: Radiology, Pathology, Dermatology, Cardiology, Surgery, Patient transport, and so many more. But, it…
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DICOMweb and FHIR ImagingStudy Resource
Both DICOM images and EHR data are now accessible via RESTful services using DICOMweb and FHIR. EHRs and other applications…
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DICOM for Informaticists 2
DICOM, the unique data format for many medical images, provides benefits and challenges for machine learning. This deeper dive with…
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Labels for AI
Learn from CDI faculty Paul Yi, MD on the role of AI labels in medical imaging data, exploring the nuances…
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DICOM Imaging Standard
Join Kinson Ho and Peter Dobbs as they discuss the relevancy and role of DICOM in imaging informatics, going beyond…
AI and Data Science
Data science is a field of study which combines domain expertise, mathematics, statistics, and programming to extract new and useful insights from medical images and other patient data. These insights potentially improve patient care and increase business value. AI and machine learning dramatically expand the opportunities to unearth these insights.
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Primer to Bias in Artificial Intelligence
Identifying and understanding bias is vitally important for trustworthy machine learning in medical imaging. Ali Tejani, MD, describes how bias…
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Dataset Drift in Radiology
Dataset drift/shift is a challenge for all machine learning. Raym Geis, MD, introduces unique aspects of it in radiology.
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Monitoring ML in Clinical Use
Explore the advancement of AI and Data Science at the intersection of system engineering and MLOps alongside CDI faculty Vidur…
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Data – The Essential Ingredient in Probability Based Algorithms
Discover the importance of quality data, the principles of data-based use cases, and risks of biases in working with data…