The use cases will increase adoption by ensuring that AI algorithms address relevant clinical questions, can be implemented across multiple electronic workflow systems, enable ongoing quality assessment, and comply with legal, regulatory, and ethical requirements.
The use cases will make it easier for developers to create algorithms that provide specific information medical professionals need and that can be implemented into clinical practice, said Rik Primo, manager of imaging informatics strategic relationships at Siemens Healthineers.
The continually updated, freely available series of use cases is the product of a collaborative framework that enables the efficient creation, implementation, and ongoing improvement of radiological AI tools, according to the institute. Specifically, the Technology Oriented Use Cases in Healthcare - AI (TOUCH-AI) framework uses multispecialty, multi-industry expert panels to define clinically relevant use cases for the development of medical imaging, interventional radiology, and radiation oncology AI algorithms. It also establishes a methodology and provides tools and metrics for creating algorithm training, testing, and validation of datasets around these use cases, the ACR DSI said.
The TOUCH-AI framework also develops standardized pathways for implementing AI algorithms in clinical practice, creates opportunities to monitor the effectiveness of AI algorithms in clinical practice, and addresses regulatory, legal, and ethical issues regarding medical imaging, interventional radiology, and radiation oncology AI.
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