Clinical translation of machine learning for medical imaging

Curtis P. Langlotz

doi:10.1117/12.3012610

3 April 2024 Clinical translation of machine learning for medical imaging

Curtis P. Langlotz

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292703 (2024) https://doi.org/10.1117/12.3012610
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Artificial intelligence and machine learning (AI/ML) are powerful tools for building computer vision systems that support the work of clinicians, leading to high interest and explosive growth in the use of these methods to analyze clinical images. These promising AI techniques create computer vision systems that perform some image interpretation tasks at the level of expert radiologists. In radiology, deep learning methods have been developed for image reconstruction, imaging quality assurance, imaging triage, computer-aided detection, computer-aided classification, and radiology documentation. The resulting computer vision systems are being implemented now and have the potential to provide real-time assistance, thereby reducing diagnostic errors, improving patient outcomes, and reducing costs. We will show examples of real-world AI applications that indicate how AI will change the practice of medicine and illustrate the breakthroughs, setbacks, and lessons learned that are relevant to medical imaging.

Conference Presentation

Citation Download Citation

Curtis P. Langlotz "Clinical translation of machine learning for medical imaging", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292703 (3 April 2024); https://doi.org/10.1117/12.3012610

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