Module #1 Introduction to AI in Radiology Overview of AI, machine learning, and deep learning in radiology, and their potential impact on the field
Module #2 Fundamentals of Machine Learning Basic concepts of machine learning, including supervised and unsupervised learning, neural networks, and data preprocessing
Module #3 Deep Learning in Radiology Introduction to deep learning, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
Module #4 Image Processing and Analysis Overview of image processing techniques, including filtering, segmentation, and feature extraction
Module #5 Radiology Image Modalities Overview of different radiology image modalities, including X-ray, CT, MRI, and ultrasound
Module #6 AI Applications in Chest Radiography Applications of AI in chest radiography, including detection of lung nodules and pneumothorax
Module #7 AI Applications in Mammography Applications of AI in mammography, including detection of breast cancer and lesion characterization
Module #8 AI Applications in CT Imaging Applications of AI in CT imaging, including detection of liver lesions and cardiovascular disease
Module #9 AI Applications in MRI Applications of AI in MRI, including detection of brain tumors and joint disorders
Module #10 AI Applications in Ultrasound Applications of AI in ultrasound, including detection of liver disease and thyroid nodules
Module #11 Computer-Aided Detection (CAD) and Diagnosis (CADx) Overview of CAD and CADx systems, including their strengths and limitations
Module #12 Image Segmentation and Registration Techniques for image segmentation and registration, including applications in radiology
Module #13 Radiomics and Radiogenomics Overview of radiomics and radiogenomics, including their applications in personalized medicine
Module #14 AI-Assisted Image Interpretation Applications of AI in image interpretation, including decision support systems and clinical workflow optimization
Module #15 AI in Radiology Workflow and Clinical Decision Making Applications of AI in radiology workflow and clinical decision making, including prioritization of exams and prediction of patient outcomes
Module #16 Ethical and Regulatory Considerations Ethical and regulatory considerations for AI in radiology, including data privacy and algorithm bias
Module #17 Building and Validating AI Models in Radiology Practical considerations for building and validating AI models in radiology, including data curation and model evaluation
Module #18 AI in Radiation Therapy Applications of AI in radiation therapy, including treatment planning and outcome prediction
Module #19 AI in Nuclear Medicine Applications of AI in nuclear medicine, including image analysis and quantification
Module #20 AI in Interventional Radiology Applications of AI in interventional radiology, including guidance and navigation systems
Module #21 AI in Radiology Education and Training Applications of AI in radiology education and training, including simulation-based learning and personalized feedback
Module #22 AI and the Future of Radiology Future directions and possibilities for AI in radiology, including potential impact on the field and workforce
Module #23 Case Studies in AI Applications in Radiology Real-world examples of AI applications in radiology, including implementation and outcomes
Module #24 AI in Radiology Research and Development Overview of current research and development in AI in radiology, including new techniques and applications
Module #25 Course Wrap-Up & Conclusion Planning next steps in AI Applications in Radiology career
