AI and machine learning in radiology: 4 things to know

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As industry experts continue to explore artificial intelligence (AI) applications in radiology, the question remains of whether AI applications can and will add value, including in new knowledge and information to provide patients with better outcomes at lower costs. In a new editorial published in JACR by a team of researchers from the department of radiology at Massachusetts General Hospital and Harvard Medical School in Boston, the "big data" consuming technologies of AI and machine learning are evaluated in terms of opportunities, challenges, pitfalls and criteria for success. "For radiologists, adding value includes establishment of more efficient work processes and improved job satisfaction," said lead author of the study James H. Thrall, MD, chairman emeritus of the department of radiology at Massachusetts General Hospital. "The goal of this perspective is to help create a framework, apart from a discussion of AI technology per se, for developing strategies to explore the potential of AI in radiology and to identify a number of scientific, cultural, educational and ethical issues that need to be addressed." The researchers note that although the ultimate role of AI in medicine is not yet clear, AI will provide advanced tools to more thoroughly analyze imaging data.