Machine learning (ML) technology has gained popularity in recent years, but its use in healthcare remains largely limited to proof-of-concept academic studies, according to a new study published in Artificial Intelligence in Medicine.
“AI has the potential to profoundly transform medical practice by aiding physicians’ interpretation of complex and diverse data types,” wrote lead author David Ben-Israel, department of clinical neurosciences at the University of Calgary, and colleagues. “If AI successfully translates into a busy clinician’s practice, it stands to improve the performance of diagnosis, prognostication and management decisions.”
So will ML translate into a busy practice? Ben-Israel and colleagues aimed to track the progression of ML implementation in modern health systems, searching through original studies on the topic published between Jan. 1, 2000, and May 1, 2018.
All studies were published in English and specifically examined the use of ML to improve patient care. Editorials, book chapters, white papers, case reports, conference abstracts and other similar documents were all excluded.
Overall, 386 publications were identified that involved the implementation of a ML strategy “to address a specific clinical problem.” Ninety-eight percent of those studies were retrospective. The authors wrote that ML stands to be a true game-changer for healthcare, but certain limitations remain that must be addressed.
“Access to real-time clinical data, data security, physician approval of ‘black box’ generated results, and performance evaluation are important aspects of implementing a ML based data strategy,” Ben-Israel et al. concluded. “Not all clinical problems will be amenable to an AI based data strategy. The careful definition of a clinical problem and the gathering of requisite data for analysis are important first steps in determining if computer science methods within medicine may advance what human intelligence has been able to accomplish.”