The Future of Automated Mobile Eye Diagnosis
Cassie A. Ludwig, BS1, Mia X. Shan, BS, BAH1, Nam Phuong H. Nguyen1, Daniel Y. Choi, MD1, Victoria Ku, BS1, Carson K. Lam, MD1
1Byers Eye Institute, Stanford University School of Medicine 2405 Watson Drive, Palo Alto, CA, USA 94305
Corresponding Author: carsonl@stanford.edu
Journal MTM 5:2:44–50, 2016
doi: 10.7309/jmtm.5.2.7
The current model of ophthalmic care requires the ophthalmologist’s involvement in data collection, diagnosis, treatment planning, and treatment execution. We hypothesize that ophthalmic data collection and diagnosis will be automated through mobile devices while the education, treatment planning, and fine dexterity tasks will continue to be performed at clinic visits and in the operating room by humans. Comprehensive automated mobile eye diagnosis includes the following steps: mobile diagnostic tests, image collection, image recognition and interpretation, integrative diagnostics, and user-friendly, mobile platforms. Completely automated mobile eye diagnosis will require improvements in each of these components, particularly image recognition and interpretation and integrative diagnostics. Once polished and integrated into greater medical practice, automated mobile eye diagnosis has the potential to increase access to ophthalmic care with reduced costs, increased efficiency, and increased accuracy of diagnosis.
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