Skip to main content

A Clinician’s Introduction to Artificial Intelligence

  • Chapter
  • First Online:
Artificial Intelligence and Ophthalmology

Part of the book series: Current Practices in Ophthalmology ((CUPROP))

  • 443 Accesses

Abstract

In this digital age, artificial intelligence and its applications have become ubiquitous in the world around us. The practice of modern medicine driven by scientific data and evidence is an obvious target for these applications. In this chapter, we explore the history of artificial intelligence, where we are now, how to interpret current evidence generated by algorithms and how to balance the hype and potential that comes with introduction of a new standard of care.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. 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(1):118.

    Article  Google Scholar 

  2. Software as a Medical Device (SAMD): clinical evaluation—guidance 2018 [updated 2018/08/31/]. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/software-medical-device-samd-clinical-evaluation.

  3. Abramoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–6.

    Article  Google Scholar 

  4. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1(1):39.

    Article  Google Scholar 

  5. Grzybowski A, Brona P. A pilot autonomous AI-based DR screening in Poland. Acta Ophthalmol. 2019;97(S263)

    Google Scholar 

  6. Projects CtW. Abacus—Wikipedia 2020 [updated 2020/11/02/]. Available from: https://en.wikipedia.org/w/index.php?title=Abacus&oldid=986730574.

  7. Projects CtW. Antikythera mechanism—Wikipedia 2020 [updated 2020/11/03/]. Available from: https://en.wikipedia.org/w/index.php?title=Antikythera_mechanism&oldid=986880521.

  8. Jensen T, editor. Ramon Llull’s Ars magna. Cham: Springer; 2018.

    Google Scholar 

  9. Racter—visual melt 2020 [updated 2020/11/05/]. Available from: https://visualmelt.com/Racter.

  10. Park E. What a difference the difference engine made: from Charles Babbage’s calculator emerged today’s computer. Smithsonian Magazine; 1996.

    Google Scholar 

  11. Ada Lovelace: founder of scientific computing 1998 [updated 1998/03/24/]. Available from: https://www.sdsc.edu/ScienceWomen/lovelace.html.

  12. Boole G. An investigation of the laws of thought: on which are founded the mathematical theories of logic and probabilities. Walton and Maberly; 1854.

    Google Scholar 

  13. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol. 1990;52(1–2):99–115. discussion 73–97

    Article  CAS  Google Scholar 

  14. Levy S. The brief history of the ENIAC computer. Smithsonian Magazine; 2013.

    Google Scholar 

  15. Computers on board the Apollo spacecraft 2014 [updated 2014/07/04/]. Available from: https://history.nasa.gov/computers/Ch2-5.html.

  16. Your smartphone is millions of times more powerful that all of NASA’s combined computing in 1969 2020 [updated 2020/02/11/]. Available from: https://www.zmescience.com/science/news-science/smartphone-power-compared-to-apollo-432.

  17. Rajaraman V. JohnMcCarthy—father of artificial intelligence. Resonance. 2014;19(3):198–207.

    Article  Google Scholar 

  18. History of AI winters. Actuaries Digital 2020 [updated 2020/11/06/]. Available from: https://www.actuaries.digital/2018/09/05/history-of-ai-winters/#_ednref2.

  19. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386–408.

    Article  CAS  Google Scholar 

  20. Arthur Samuel 2007 [updated 2007/12/08/]. Available from: http://infolab.stanford.edu/pub/voy/museum/samuel.html.

  21. Eliza, a chatbot therapist 2018 [updated 2018/07/18/]. Available from: https://web.njit.edu/~ronkowit/eliza.html.

  22. van Melle W. MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Int J Man-Mach Stud. 1978;10(3):313–22.

    Article  Google Scholar 

  23. Cintula P, Fermüller CG, Noguera C. Fuzzy logic 2016 [updated 2016/11/15/]. Available from: https://plato.stanford.edu/entries/logic-fuzzy.

  24. Lighthill report 2017 [updated 2017/12/20/]. Available from: http://www.chilton-computing.org.uk/inf/literature/reports/lighthill_report/contents.htm.

  25. Weizenbaum J. Computer power and human reason: W.H. Freeman & Company; 1976.

    Google Scholar 

  26. Pollack A. Setbacks for artificial intelligence. NY Times; 1988.

    Google Scholar 

  27. Waibel A, Hanazawa T, Hinton G, Shikano K, Lang KJ. Phoneme recognition using time-delay neural networks. Readings in speech recognition. Morgan Kaufmann Publishers Inc.; 1990. p. 393–404.

    Google Scholar 

  28. Moravec H. Mind children. Cambridge, MA: Harvard University Press; 1990.

    Google Scholar 

  29. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1(4):541–51.

    Article  Google Scholar 

  30. Play Chinook—World man- machine checkers champion 2012 [updated 2012/03/08/]. Available from: https://webdocs.cs.ualberta.ca/~chinook/play.

  31. IBM100—Deep blue. IBM corporation; 2012 [updated 2012/03/07/]. Available from: https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue.

  32. LOGISTELLO’ homepage 2011 [updated 2011/02/03/]. Available from: https://skatgame.net/mburo/log.html.

  33. Oh K-S, Jung K. GPU implementation of neural networks. Pattern Recogn. 2004;37(6):1311–4.

    Article  Google Scholar 

  34. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. IEEE.

    Google Scholar 

  35. The DeepQA research team—IBM 2020 [updated 2020/11/06/]. Available from: https://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=2160.

  36. AlphaGo Zero: starting from scratch 2020 [updated 2020/11/06/]. Available from: https://deepmind.com/blog/article/alphago-zero-starting-scratch.

  37. Brown T, Mane D, Roy A, Abadi M, Gilmer J. Adversarial patch. Google Research; 2017.

    Google Scholar 

  38. Su J, Vargas DV, Kouichi S. One pixel attack for fooling deep neural networks. arXiv. 2017.

    Google Scholar 

  39. Avati A, Jung K, Harman S, et al. Improving palliative care with deep learning. arXiv. 2017.

    Google Scholar 

  40. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.

    Article  Google Scholar 

  41. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv. 2014.

    Google Scholar 

  42. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. arXiv. 2014.

    Google Scholar 

  43. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv. 2015.

    Google Scholar 

  44. Datasets. Deep learning 2020 [updated 2020/11/06/]. Available from: http://deeplearning.net/datasets.

  45. Artificial intelligence—Search results—PubMed 2020 [updated 2020/11/06/]. Available from: https://pubmed.ncbi.nlm.nih.gov/?term=artificial+intelligence&filter=years.1951-2020&timeline=expanded.

  46. Hype cycle research methodology 2020 [updated 2020/11/06/]. Available from: https://www.gartner.com/en/research/methodologies/gartner-hype-cycle.

  47. Machine learning—Search results—PubMed 2020 [updated 2020/11/06/]. Available from: https://pubmed.ncbi.nlm.nih.gov/?term=machine+learning&filter=years.1957-2020&timeline=expanded.

  48. Deep learning—Search Results—PubMed 2020 [updated 2020/11/06/]. Available from: https://pubmed.ncbi.nlm.nih.gov/?term=deep+learning&filter=years.1954-2020&timeline=expanded.

  49. Carin L, Pencina MJ. On deep learning for medical image analysis. JAMA. 2018;320(11):1192–3.

    Article  Google Scholar 

  50. Yu M, Tham Y-C, Rim TH, et al. Reporting on deep learning algorithms in health care. Lancet Digit Health. 2019;1(7):e328–e9.

    Article  Google Scholar 

  51. Liu Y, Chen P-HC, Krause J, Peng L. How to read articles that use machine learning: users’ guides to the medical literature. JAMA. 2019;322(18):1806–16.

    Article  Google Scholar 

  52. Jaeschke R, Guyatt GH, Sackett DL, et al. Users’ guides to the medical literature: III. How to use an article about a diagnostic test B. what are the results and will they help me in caring for my patients? JAMA. 1994;271(9):703–7.

    Article  CAS  Google Scholar 

  53. 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(9):1342–50.

    Article  Google Scholar 

  54. Moraes G, Fu DJ, Wilson M, et al. Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning. Ophthalmology. https://doi.org/10.1016/j.ophtha.2020.09.025.

  55. Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318(6):517–8.

    Article  Google Scholar 

  56. Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: humanism and artificial intelligence. JAMA. 2018;319(1):19–20.

    Article  Google Scholar 

  57. Wang F, Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA Intern Med. 2019;179(3):293–4.

    Article  Google Scholar 

  58. Price WN II, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. 2019;322(18):1765–6.

    Article  Google Scholar 

  59. Mandl KD, Manrai AK. Potential excessive testing at scale: biomarkers, genomics, and machine learning. JAMA. 2019;321(8):739–40.

    Article  Google Scholar 

  60. Eveleth R. The Atlantic 2018 [updated 2018/04/05/]. Available from: https://www.theatlantic.com/technology/archive/2014/12/how-self-tracking-apps-exclude-women/383673.

  61. How IBM Watson overpromised and underdelivered on AI health care—IEEE Spectrum 2020 [updated 2020/11/06/]. Available from: https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care.

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Thakur, S., Cheng, CY. (2021). A Clinician’s Introduction to Artificial Intelligence. In: Ichhpujani, P., Thakur, S. (eds) Artificial Intelligence and Ophthalmology. Current Practices in Ophthalmology. Springer, Singapore. https://doi.org/10.1007/978-981-16-0634-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-0634-2_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0633-5

  • Online ISBN: 978-981-16-0634-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics