*Unlocking the Potential of Artificial Intelligence (AI) for Healthcare DOI: http://dx.doi.org/10.5772/intechopen.111489*

Additionally, AI can also be used for automated grading and feedback for radiology residents, which can save time for educators and provide more accurate and consistent feedback to the residents. Overall, the future of AI in radiology resident education will be closely tied to the development of AI-ML curricula and precision medical education encompassing the three learning theories (behaviorist, cognitive, and constructivist), which will enable residents to learn the latest techniques and technologies in an effective and efficient way and personalize the learning experience based on the residents' needs [24].

A recently developed elective in data science pathway (DSP) for fourth-year radiology residents at Brigham and Women's Hospital (BWH) in Boston has the potential to prepare the next generation of radiologists to lead the way in artificial intelligence and machine learning (AI-ML) [28]. The resident feedback from the pilot resulted in the establishment of a formal AI-ML curriculum for future residents, which included logistical, planning, and curricular considerations for DSP implementation at other institutions [28].

In summary, AI has the potential to greatly enhance radiology resident education by providing new tools and resources for teaching and learning, improving diagnostic skills, providing hands-on training experiences, and personalized learning experiences, as well as automated grading and feedback.

AI will impact radiology such asmany other medical fields, but radiologists can play a leading role in this forthcoming change by reducing the huge amount of data and information into the most relevant information.
