Rapid advances in AI in ophthalmology are a harbinger of things to come for other fields of medicine
The autonomous detection and triage of eye disease, or even accurate estimations of gender, age, and blood pressure from a simple retinal photo, may sound like the realms of science fiction, but advances in artificial intelligence (AI) have already made this a reality.1 Ophthalmology is at the vanguard of the development and clinical application of AI. Advances in the field may provide useful insights into the application of this technology in health care more broadly.
The full article is accessible to AMA members and paid subscribers. Login to read more or purchase a subscription now.
Please note: institutional and Research4Life access to the MJA is now provided through Wiley Online Library.
- 1. Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2018; 2: 158.
- 2. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. London: Hachette UK, 2019.
- 3. Schmidt‐Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67: 1–29.
- 4. Ting DSW, Cheung CY‐L, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318: 2211–2223.
- 5. Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision‐threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 2018; 41: 2509–2516.
- 6. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI‐based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018; 1: 39.
- 7. De Fauw J, Ledsam JR, Romera‐Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018; 24: 1342–1350.
- 8. Bogunović H, Waldstein SM, Schlegl T, et al. Prediction of anti‐VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 2017; 58: 3240–3248.
- 9. Schmidt‐Erfurth U, Waldstein SM, Klimscha S, et al. Prediction of individual disease conversion in early AMD using artificial intelligence. Invest Ophthalmol Vis Sci 2018; 59: 3199–3208.
- 10. Wen JC, Lee CS, Keane PA, et al. Forecasting future Humphrey visual fields using deep learning. PLoS One 2019; 14: e0214875.
- 11. Arcadu F, Benmansour F, Maunz A, et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med 2019; 2: 1–9.
- 12. Bellemo V, Lim G, Rim TH, et al. Artificial intelligence screening for diabetic retinopathy: the real‐world emerging application. Curr Diab Rep 2019; 19: 72.
- 13. Hern A. Google DeepMind 1.6 m patient record deal “inappropriate”. https://www.theguardian.com/technology/2017/may/16/google-deepmind-16m-patient-record-deal-inappropriate-data-guardian-royal-free (viewed Jan 2021). The Guardian 2017; 16 May.
- 14. Sayres R, Taly A, Rahimy E, et al. Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 2019; 126: 552–564.
- 15. O’Sullivan S, Nevejans N, Allen C, et al. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int J Med Robot 2019; 15: e1968.
- 16. Hajkowicz SA, Karimi S, Wark T, et al. Artificial Intelligence: solving problems, growing the economy and improving our quality of life. CSIRO Data61, 2019. https://data61.csiro.au/en/Our-Research/Our-Work/AI-Roadmap (viewed Jan 2021).
- 17. Walsh T, Levy N, Bell G, et al. The effective and ethical development of artificial intelligence: an opportunity to improve our wellbeing. Report for the Australian Council of Learned Academies. ACOLA 2019. https://acola.org/wp-content/uploads/2019/07/hs4_artificial-intelligence-report.pdf (viewed Jan 2021).
- 18. Australian Academy of Health and Medical Sciences. Response to the Department of Industry, Innovation and Science Consultation on the Artificial Intelligence. Australia’s Ethics Framework, May 2019. https://aahms.org/wp-content/uploads/2019/06/AAHMS_Consultation-Response_Artificial-Intelligence-Australias-Ethics-Framework.pdf (viewed Jan 2021).
- 19. Royal Australian and New Zealand College of Radiologists. Member Consultation: RANZCR Standards of Practice for Artificial Intelligence; 25 Sept 2019. https://www.ranzcr.com/fellows/clinical-radiology/professional-documents/standards-of-practice-for-artificial-intelligence (viewed Jan 2021).
Online responses are no longer available. Please refer to our instructions for authors page for more information.
The Centre for Eye Research Australia receives operational and infrastructure support from the Victorian State Government. Jane Scheetz is supported by a Melbourne Academic Centre for Health Translational Research Fellowship.
No relevant disclosures.