AI Essential for Educating Next Generation of Medical Professionals

A new viewpoint study suggests that educating the next generation of medical professionals with the right machine learning (ML) techniques will enable them to become part of the emerging data science revolution.

Researchers at Boston University School of Medicine (BUMC; MA, USA) searched PubMed to uncover the number of papers published in the area of ML since the beginning of this decade. They found that while the number had increased, the number of publications related to undergraduate and graduate medical education in the field remained relatively unchanged since 2010, with a grand total of just 16 studies. Realizing the need for educating the students and trainees within BUMC about ML, the researchers designed and taught an introductory course.

The course is intended to educate the next generation of medical professionals and young researchers with biomedical and life sciences backgrounds about ML concepts to help prepare them for the ongoing data science revolution. The researchers believe that if medical education begins to implement a ML curriculum, physicians may begin to recognize the conditions and future applications where AI could potentially benefit clinical decision-making and management early on in their career, and be ready to utilize these tools better when beginning practice. The study was published as a perspective in the September 2018 issue of NPJ Digital Medicine.

“The general public has become quite aware of AI and the impact it can have on health care outcomes such as providing clinicians with improved diagnostics. However, if medical education does not begin to teach medical students about AI and how to apply it into patient care, then the advancement of technology will be limited in use and its impact on patient care,” said lead author Vijaya Kolachalama, PhD. “Technology without physician knowledge of its potential and applications does not make sense and will only further perpetuate healthcare costs.”

The rising popularity of ML techniques for medical applications is evident from the increasing amount of research, the number of products obtaining regulatory approvals, and entrepreneurial efforts over the past few years. Venture capital funding for healthcare AI startup companies was about USD 3.6 billion in the last five years, underscoring the increasing appreciation of the value that ML can potentially bring to the medical community.

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Boston University School of Medicine