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Artificial Intelligence & Machine Learning in Clinical Medicine

Chatbots using artificial intelligence (AI) have the potential to become important tools in the practice of medicine, according to the authors of a review article.

“We firmly believe that the introduction of AI and machine learning in medicine has helped health professionals improve the quality of care that they can deliver and has the promise to improve it even more in the near future and beyond,” the authors conclude. “Just as computer acquisition of radiographic images did away with the x-ray file room and lost images, AI and machine learning can transform medicine. Health professionals will figure out how to work with AI and machine learning as we grow along with the technology. AI and machine learning will not put health professionals out of business; rather, they will make it possible for health professionals to do their jobs better and leave time for the human–human interactions that make medicine the rewarding profession we all value.”

Despite their promise, AI and chatbots have limitations and must be used appropriately, the authors add: “Like any good tool, they can help us do our job better, but if not used properly, they have the potential to do damage. Since the tools are new and hard to test with the use of [traditional methods], the medical community will be learning how to use them, but learn we must. There is no question that the chatbots will also learn from their users. Thus, we anticipate a period of adaptation by both the user and the tool.”

Editorial: “Medicine is much different from other areas where AI is being applied,” editorialists write. “AI enables new discoveries and improved processes in the entire health care continuum; ethical, governance, and regulatory considerations are critical in the design, implementation, and integration of every component of the AI applications and systems. Because of concerns about both utility and safety, new applications will generally have to adhere to the same standards applied to other medical technologies. This will require a level of rigor in testing similar to that used in other areas of medicine, but it also can present challenges, such as the ‘dataset shift’ that can result when there is a mismatch between the data set with which an AI system was developed and the data on which it is being deployed.”

Source: New England Journal of Medicine