Seminars in Roentgenology

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There is a steadily increasing number of artificial intelligence (AI) tools available and cleared for use in clinical radiological practice. Radiologists will increasingly be faced with options provided by other radiologist colleagues, clinician colleagues, vendors, or other professionals for obtaining and deploying AI algorithms in clinical practice. It is important that radiologists are familiar with basic and practical aspects that need to be considered when assessing an AI tool for use in their practice, so that resources are properly allocated and there is an appropriate return on investment through enhancements in patient quality of care, safety, and/or process efficiency...

In the field of radiology, the use of artificial intelligence (AI) is increasing. Even though healthcare facilities are interested in using this technology, having success with an AI project can be challenging. There is a myriad of AI solutions today, and comparing them can be challenging. Moreover, the implementation process involves alignment with many different areas. In our institution, we have been testing, developing, deploying, and monitoring AI solutions for the last four years. This article intends to share our experience and highlight the most important points to ensure a successful project based on our experience in the setting of a large private practice in Latin America...

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Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists...