Skip to main content
SpringerLink
Log in

Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges

  • Chapter
  • First Online:
Artificial Intelligence in Ophthalmology

  • 910 Accesses

Abstract

The chapter presents the overview of the present achievements and applications of artificial intelligence in medicine, including the possible benefits for future medicine. It also presents the increase in published scientific studies using artificial intelligence in medicine in last decade.

The regulation frameworks for medical devices, including AI medical devices, in the USA and in the European Union is discussed. Moreover, the problem of access to reliable data is given with the explanation of transfer learning, generative adversarial networks, and continual machine learning. Some of the hazards, and challenges of the AI in ophthalmology are described, including privacy protection, testing AI algorithms on data sets that did not correspond real-world conditions, and its vulnerability to cybersecurity attacks. Finally, some aspects of cost-effectiveness of AI-based devices are presented.

“If you do not get feedback, your confidence grows much faster than your accuracy”

Tetlock P., Gardner D. Superforcasting: The Art and Science of Prediction, Crown Publishing, 2016.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mitchell M. Artificial intelligence: a guide for thinking humans. Penguin UK; 2019.

    Google Scholar 

  2. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. New York: Basic Books; 2019.

    Google Scholar 

  3. Copeland BJ. Artificial intelligence. Encyclopedia Britannica, 11 August 2020. https://www.britannica.com/technology/artificial-intelligence. Accessed 18 Mar 2021.

  4. Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014;23(9):727–31.

    Article  Google Scholar 

  5. Gunderson CG, Bilan VP, Holleck JL, et al. Prevalence of harmful diagnostic errors in hospitalised adults: a systematic review and meta-analysis. BMJ Qual Saf. 2020;29:1008–18.

    Article  Google Scholar 

  6. Zwaan L, Singh H. Diagnostic error in hospitals: finding forests not just the big trees. BMJ Qual Saf. 2020;29(12):961–4.

    Article  Google Scholar 

  7. Singletary B, Patel N, Heslin M. Patient perceptions about their physician in 2 words: the good, the bad, and the ugly. JAMA Surg. 2017;152(12):1169–70.

    Article  Google Scholar 

  8. Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3:118.

    Article  Google Scholar 

  9. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158–64.

    Article  Google Scholar 

  10. Chen J, See KC. Artificial intelligence for COVID-19: rapid review. J Med Internet Res. 2020;22(10):e21476.

    Article  Google Scholar 

  11. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care. 2021;dc201877. https://doi.org/10.2337/dc20-1877.

  12. Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health. 2021;3(3):e195–203.

    Article  Google Scholar 

  13. Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA. 2019;322(23):2285–6.

    Article  Google Scholar 

  14. Hwang TJ, Sokolov E, Franklin JM, Kesselheim AS. Comparison of rates of safety issues and reporting of trial outcomes for medical devices approved in the European Union and United States: cohort study. BMJ. 2016;353:i3323.

    Article  Google Scholar 

  15. European Commission. Medical devices—EUDAMED. 17 June 2020. https://ec.europa.eu/growth/sectors/medical-devices/new-regulations/eudamed_en. Accessed 15 Jan 2021.

  16. Ting DSW, Liu Y, Burlina P, Xu X, Bressler NM, Wong TY. AI for medical imaging goes deep. Nat Med. 2018;24(5):539–40.

    Article  CAS  Google Scholar 

  17. Wang SY, Pershing S, Lee AY, AAO Taskforce on AI and AAO Medical Information Technology Committee. Big data requirements for artificial intelligence. Curr Opin Ophthalmol. 2020;31(5):318–23.

    Article  Google Scholar 

  18. Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131.e9.

    Article  CAS  Google Scholar 

  19. Rampasek L, Goldenberg A. Learning from everyday images enables expert-like diagnosis of retinal diseases. Cell. 2018;172(5):893–5.

    Article  CAS  Google Scholar 

  20. Burlina P, Paul W, Mathew P, Joshi N, Pacheco KD, Bressler NM. Low-shot deep learning of diabetic retinopathy with potential applications to address artificial intelligence bias in retinal diagnostics and rare ophthalmic diseases. JAMA Ophthalmol. 2020;138(10):1070–7.

    Article  Google Scholar 

  21. Burlina PM, Joshi N, Pacheco KD, Liu TYA, Bressler NM. Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmol. 2019;137:258–64.

    Article  Google Scholar 

  22. Liu Y, Yang J, Zhou Y, Wang W, Zhao J, Yu W, Zhang D, Ding D, Li X, Chen Y. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network. Br J Ophthalmol. 2020;104(12):1735–40.

    Article  Google Scholar 

  23. Zheng C, Xie X, Zhou K, Chen B, Chen J, Ye H, Li W, Qiao T, Gao S, Yang J, Liu J. Assessment of generative adversarial networks model for synthetic optical coherence tomography images of retinal disorders. Transl Vis Sci Technol. 2020;9(2):29.

    Article  Google Scholar 

  24. Liu TYA, Farsiu S, Ting DS. Generative adversarial networks to predict treatment response for neovascular age-related macular degeneration: interesting, but is it useful? Br J Ophthalmol. 2020;104(12):1629–30.

    Article  Google Scholar 

  25. Lee CS, Lee AY. Clinical applications of continual learning machine learning. Lancet Digit Health. 2020;2(6):e279–81.

    Article  Google Scholar 

  26. Mehta N, Lee CS, Mendonça LSM, Raza K, Braun PX, Duker JS, Waheed NK, Lee AY. Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017–24.

    Article  Google Scholar 

  27. Larson DB, Harvey H, Rubin DL, Irani N, Tse JR, Langlotz CP. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol. 2021;18(3 Pt A):413–24.

    Article  Google Scholar 

  28. Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. Inconsistent performance of deep learning models on mammogram classification. J Am Coll Radiol. 2020;17:796–803.

    Article  Google Scholar 

  29. Subbaswamy A, Schulam P, Saria S. Preventing failures due to dataset shift: Learning predictive models that transport. Proc Mach Learn Res. 2019;89:3118–27.

    Google Scholar 

  30. Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21:345–52.

    PubMed  Google Scholar 

  31. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.

    Article  Google Scholar 

  32. Winkler JK, Fink C, Toberer F. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 2019;155:1135–41.

    Article  Google Scholar 

  33. Finlayson SG, Bowers JD, Ito J. Adversarial attacks on medical machine learning. Science. 2019;363:1287–9.

    Article  CAS  Google Scholar 

  34. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018;15:e1002683.

    Article  Google Scholar 

  35. Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci U S A. 2020; pii: 201907377. https://doi.org/10.1073/pnas.1907377117.

  36. Matheny ME, Thadaney Israni S, Ahmed M, Whicher D. AI in health care: the hope, the hype, the promise, the peril. Washington, DC: National Academy of Medicine; 2019. https://nam.edu/artificial-intelligence-special-publication

    Google Scholar 

  37. Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E, Obermeyer Z, Yu B, Butte AJ. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320–4.

    Article  CAS  Google Scholar 

  38. Floridi L, Cowls J, King TC, Taddeo M. How to design AI for social good: seven essential factors. Sci Eng Ethics. 2020;26(3):1771–96.

    Article  Google Scholar 

  39. Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, Yip MYT, Qi Lee X, Hsu W, Li Lee M, Tan CS, Tym Wong H, Lamoureux EL, Tan GSW, Wong TY, Finkelstein EA, Ting DSW. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health. 2020;2(5):e240–9.

    Article  Google Scholar 

  40. Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda S, Taylor P, Rudnicka AR. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124(3):343–51.

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank Aleksandra Lemanik, Foundation for Ophthalmology Development, Poznan, Poland and Tomasz Krzywicki, Faculty of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn, Poland for their help in preparing illustrations, and Szymon Wilk, Faculty of Computing and Telecommunications, Poznan University of Technology, Poznan, Poland, for his valuable discussion on this chapter.

Author information

Authors and Affiliations

  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

  2. Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland

    Andrzej Grzybowski

Authors
  1. Andrzej Grzybowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Grzybowski, A. (2021). Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78601-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78600-7

  • Online ISBN: 978-3-030-78601-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation