Medical Education Must Move From the Information Age to the Age of Artificial Intelligence

By S. Wartman, C. Combs. Academic medicine : journal of the Association of American Medical Colleges, 2017

374 citations recorded by the source corpus.

Noteworthy changes coming to the practice of medicine require significant medical education reforms. While proposals for such reforms abound, they are insufficient because they do not adequately address the most fundamental change—the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Increasingly, future medical practice will be characterized by: the delivery of care wherever the patient happens to be; the provision of care by newly constituted health care teams; the use of a growing array of data from multiple sources and artificial intelligence applications; and the skillful management of the interface between medicine and machines. To be effective in this environment, physicians must work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients, given their unique human complexities. The authors believe that a “reboot” of medical education is required that makes better use of the findings of cognitive psychology and pays more attention to the alignment of humans and machines in education and practice. Medical education needs to move beyond the foundational biomedical and clinical sciences. Systematic curricular attention must focus on the organization of professional effort among health professionals, the use of intelligence tools involving large data sets, and machine learning and robots, all the while assuring the mastery of compassionate care.

Subfields: Family Practice, Public Health, Environmental and Occupational Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/33ee99463e2258c600f05e7b212d19d227adfac9

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mutlu Cukurova
2. Kai Siang Chan
3. Moriah Ariely
4. Michiel Schinkel
5. Zhijian Lin
6. Prabath W. B. Nanayakkara
7. Lijia Chen
8. N. Zary

Citations in the corpus

• Introducing Artificial Intelligence Training in Medical Education (2019, positive): “These advances and transitioning from an information age to the age of AI [56] change clinical practice and patient outcomes for the better.”
• An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals (2023, neutral): “In medical education, the necessity to provide training in AI beyond clinical and biomedical skills is emphasized 70,71 .”
• Artificial Intelligence in Education: A Review (2020, background): “For example, there is heavy use of AI in the medical profession, which necessitates exposing students to AI through use of the technology in medical education to prepare them for the experiences in the real world [17].”
• Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review (2019, background): “Currently, there are limited articles [9,10] discussing or reviewing the current applications of AI in medical education.”
• Teachers' trust in AI-powered educational technology and a professional development program to improve it (2022, background): “Research confirmed that to use learning analytics effectively (i.e., critically interpret the re-sults rooted in big data analysis and make data-informed decisions), end-users need to have basic data literacy (Wolff et al., 2016).”