Article Text
Abstract
Training the modern ophthalmic surgeon is a challenging process. Microsurgical education can benefit from innovative methods to practice surgery in low-risk simulations, assess and refine skills in the operating room through video content analytics, and learn at a distance from experienced surgeons. Developments in emerging technologies may allow us to pursue novel forms of instruction and build on current educational models. Artificial intelligence, which has already seen numerous applications in ophthalmology, may be used to facilitate surgical tracking and evaluation. Within immersive technology, growth in the space of virtual reality head-mounted displays has created intriguing possibilities for operating room simulation and observation. Here, we explore the applications of these technologies and comment on their future in ophthalmic surgical education.
- Treatment Surgery
- Medical Education
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Footnotes
Contributors SKB wrote the first draft. SRL, DSWT, MFC, and JC commented on and revised the manuscript. All authors collaborated on the final version.
Funding This work was supported in part by an unrestricted grant to the Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, from Research to Prevent Blindness, New York, NY.
Competing interests SKB: none. SRL: none. DSWT is the patent holder of a deep learning system for retinal diseases, co-founder and equity holder in the EyRIS Singapore, consultant for Novartis (Basel, Switzerland), and scientific advisory board member for Ocutrx (Irvine, USA) and Optomed (Finland). MFC is a consultant for Novartis (Basel, Switzerland), an equity owner in InTeleretina, LLC (Honolulu, HI), and is supported by NIH grant EY10572 (Bethesda, MD). JC has received travel support from Santen (Osaka, Japan) and consulted for the US Food and Drug Administration. The authors were additionally supported by unrestricted departmental funding to their departments from Research to Prevent Blindness (New York, NY).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Not applicable.