摘要
Artificial intelligence(AI)has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago.These algorithms,ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks,have continuously sought to revolutionize medical imaging.With the number of imaging studies outpacing the amount of trained of readers,AI has been implemented to streamline workflow efficiency and provide quantitative,standardized interpretation.AI relies on massive amounts of data for its algorithms to function,and with the wide-spread adoption of Picture Archiving and Communication Systems(PACS),imaging data is accumulating rapidly.Current AI algorithms using machine-learning technology,or computer aided-detection,have been able to successfully pool this data for clinical use,although the scope of these algorithms remains narrow.Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage,however interpretation has generally remained a human responsibility for now.In this review article,we will summarize the current successes and limitations of AI in radiology,and explore the exciting prospects that deep-learning technology offers for the future.
