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Yapay Zekanın Göz Sağlığı Alanına Etkileri: Fırsatlar ve Zorluklar

Year 2024, Volume: 5 Issue: 2, 61 - 71, 14.06.2024
https://doi.org/10.46871/eams.1456762

Abstract

Göz hastalıklarıyla ilgilenen tıp alanı olan Oftalmoloji, yapay zekanın (YZ) ortaya çıkmasıyla birlikte dönüşüm yaşıyor. Bu derleme, YZ'nın oftalmolojik uygulamalarda, özellikle hastalık teşhisi, taraması ve cerrahi rehberlik gibi alanlarda kullanımının artmasını incelemektedir. Daha doğru, verimli ve erişilebilir bir göz bakımı sağlama yetenekleri de dahil olmak üzere, YZ destekli araçların potansiyel faydalarını araştırıyoruz. Bununla birlikte, bu teknolojiyle ilişkili etik ve uygulamaya yönelik zorluklar da incelenecektir. Bunlar arasında algoritmik önyargı(bias), açıklanabilirliğin eksikliği ve potansiyel iş kaybı yer almaktadır. Göz hekimleri ve YZ'nın hasta bakımını iyileştirmek ve oftalmoloji uygulamalarında yeni bir dönemi başlatmak için iş birliği yaptığı bir geleceği öngörüyoruz.

References

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  • 2. Yılmaz İE, Doğan L. Talking technology: exploring chatbots as a tool for cataract patient education. Clin Exp Optom. Published online 2024. doi:10.1080/08164622.2023.2298812
  • 3. Levent D, Bekir ÖG, Edhem YĬ. The Performance of Chatbots and the AAPOS Website as a Tool for Amblyopia Education. Journal of Pediatric Ophthalmology & Strabismus. 2024;0(0):1-7. doi:10.3928/01913913-20240409-01
  • 4. Rogers TW, Jaccard N, Carbonaro F, et al. Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study. Eye (Basingstoke). 2019;33(11):1791-1797. doi:10.1038/s41433-019-0510-3
  • 5. Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye (Basingstoke). 2018;32(6):1138-1144. doi:10.1038/s41433-018-0064-9
  • 6. Prashar J, Tay N. Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis. Eye. 2024;38(2):303-314. doi:10.1038/s41433-023-02680-z
  • 7. Huang Y, Cheung CY, Li D, et al. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye (Basingstoke). 2023;38(3):464-472. doi:10.1038/s41433-023-02724-4 8. Şenol A, Talan T, Aktürk C. A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis. Signal Image Video Process. 2024;18(5):4589-4603. doi:10.1007/S11760-024-03097-1/METRICS
  • 9. Srivastava O, Tennant M, Grewal P et al. Artificial intelligence and machine learning in ophthalmology: A review. Indian J Ophthalmol. 2023;71(1):11. doi:10.4103/IJO.IJO_1569_22
  • 10. He J, Cao T, Xu F, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye (Basingstoke). 2019;34(3):572-576. doi:10.1038/s41433-019-0562-4
  • 11. Rogers TW, Gonzalez-Bueno J, Garcia Franco R, et al. Evaluation of an AI system for the detection of diabetic retinopathy from images captured with a handheld portable fundus camera: the MAILOR AI study. Eye (Basingstoke). 2020;35(2):632-638. doi:10.1038/s41433-020-0927-8
  • 12. Cheung R, Chun J, Sheidow T et al. Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis. Eye (Basingstoke). 2021;36(5):994-1004. doi:10.1038/s41433-021-01540-y
  • 13. Crincoli E, Ferrara S, Miere A, et al. Correlation between AI-measured lacquer cracks extension and development of myopic choroidal neovascularization. Eye (Basingstoke). 2023;37(14):2963-2968. doi:10.1038/s41433-023-02451-w
  • 14. Korot E, Wood E, Weiner A et al. A renaissance of teleophthalmology through artificial intelligence. Eye (Basingstoke). 2019;33(6):861-863. doi:10.1038/s41433-018-0324-8
  • 15. Rajalakshmi R. The impact of artificial intelligence in screening for diabetic retinopathy in India. Eye (Basingstoke). 2019;34(3):420-421. doi:10.1038/s41433-019-0626-5
  • 16. Bahl A, Rao S. Diabetic retinopathy screening in rural India with portable fundus camera and artificial intelligence using eye mitra opticians from Essilor India. Eye (Basingstoke). 2020;36(1):230-231. doi:10.1038/s41433-020-01350-8
  • 17. Anguita R, Makuloluwa A, Hind J et al. Large language models in vitreoretinal surgery. Eye 2023 38:4. 2023;38(4):809-810. doi:10.1038/s41433-023-02751-1
  • 18. Poh SSJ, Sia JT, Yip MYT et al. Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases. Ophthalmol Retina. Published online January 26, 2024. doi:10.1016/J.ORET.2024.01.018
  • 19. Ates HC, Brunauer A, von Stetten F, et al. Integrated devices for non-invasive diagnostics. Adv Funct Mater. 2021;31(15):2010388. doi:10.1002/adfm.202010388
  • 20. Gambhir SS, Ge TJ, Vermesh O et al. Continuous health monitoring: an opportunity for precision health. Sci Transl Med. 2021;13(597):eabe5383. doi:10.1126/scitranslmed.abe5383
  • 21. Heikenfeld J, Jajack A, Rogers J, et al. Wearable sensors: modalities, challenges, and prospects. Lab Chip. 2018;18(2):217-248. doi:10.1039/c7lc00914c
  • 22. Iqbal SMA, Mahgoub I, Du E et al. Advances in healthcare wearable devices. npj Flex Electron. 2021;5(1). doi:10.1038/s41528-021-00107-x
  • 23. Zhang J, Kim K, Kim HJ, et al. Smart soft contact lenses for continuous 24-hour monitoring of intraocular pressure in glaucoma care. Nature Communications 2022 13:1. 2022;13(1):1-15. doi:10.1038/s41467-022-33254-4
  • 24. Amini P, Okeme J. Tear Fluid as a Matrix for Biomonitoring Environmental and Chemical Exposures. Published online December 6, 2023. doi:10.21203/RS.3.RS-3711147/V3
  • 25. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-2410. doi:10.1001/JAMA.2016.17216
  • 26. Xie Y, Nguyen QD, Hamzah H, et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health. 2020;2(5):e240-e249. doi:10.1016/S2589-7500(20)30060-1
  • 27. Yang K, Nambudiri VE. Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery. Appl Clin Inform. 2021;12(5):1157-1160. doi:10.1055/S-0041-1740259/ID/JR210140LE-27/BIB
  • 28. Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10 18.doi:10.1177/0840470419873123/ASSET/IMAGES/LARGE/10.1177_0840470419873123-FIG1.JPEG
  • 29. Bailey JE, Gurgol C, Pan E, et al. Early Patient-Centered Outcomes Research Experience with the Use of Telehealth to Address Disparities: Scoping Review. J Med Internet Res. 2021;23(12):e28503. doi:10.2196/28503
  • 30. Purcell WM, Burrell DN. Dynamic Evaluation Approaches to Telehealth Technologies and Artificial Intelligence (AI) Telemedicine Applications in Healthcare and Biotechnology Organizations. Merits 2023, Vol 3, Pages 700-721. 2023;3(4):700-721. doi:10.3390/MERITS3040042
  • 31. Wang C, Yao C, Chen P et al. Artificial Intelligence Algorithm with ICD Coding Technology Guided by Embedded Electronic Medical Record System in Medical Record Information Management. Microprocess Microsyst. Published online October 13, 2023:104962. doi:10.1016/J.MICPRO.2023.104962
  • 32. Young JA, Chang CW, Scales CW et al. Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study. JMIR AI 2024;3:e48295 https://ai.jmir.org/2024/1/e48295. 2024;3(1):e48295. doi:10.2196/48295
  • 33. Bhuiyan A, Wong TY, Ting DSW et al. Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD. Transl Vis Sci Technol. 2020;9(2):25-25. doi:10.1167/TVST.9.2.25
  • 34. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 2019 25:1. 2019;25(1):44-56. doi:10.1038/S41591-018-0300-7
  • 35. Luxton DD. Ethical implications of conversational agents in global public health. Bull World Health Organ. 2020;98(4):285-287. doi:10.2471/BLT.19.237636
  • 36. Evans NG, Wenner DM, Cohen IG, et al. Emerging Ethical Considerations for the Use of Artificial Intelligence in Ophthalmology. Ophthalmology Science. 2022;2(2):100141. doi:10.1016/J.XOPS.2022.100141
  • 37. Nazerid LH, Zatarah R, Waldrip S, et al. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health.2023;2(6):e0000278. doi:10.1371/JOURNAL.PDIG.0000278
  • 38. Khunte M, Chae A, Wang R, et al. Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging. Clin Radiol. 2023;78(2):123-129. doi:10.1016/J.CRAD.2022.09.122
  • 39. Li H, Moon JT, Purkayastha S et al. Ethics of large language models in medicine and medical research. Lancet Digit Health. 2023;5(6):e333-e335. doi:10.1016/S2589-7500(23)00083-3
  • 40. Triberti S, Durosini I, Pravettoni G. A “Third Wheel” Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective. Front Public Health. 2020;8:517191.doi:10.3389/FPUBH.2020.00117/BIBTEX
  • 41. Durosini I, Pizzoli SFM, Strika M et al. Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context. Artificial Intelligence for Medicine. Published online January 1, 2024:231-237. doi:10.1016/B978-0-443-13671-9.00011-9
  • 42. Khan WU, Seto E. A “Do No Harm” Novel Safety Checklist and Research Approach to Determine Whether to Launch an Artificial Intelligence–Based Medical Technology: Introducing the Biological-Psychological, Economic, and Social (BPES) Framework. J Med Internet Res. 2023;25(1):e43386. doi:10.2196/43386

The Rise of the Machines: Artificial Intelligence in Ophthalmology - A Boon or Bane?

Year 2024, Volume: 5 Issue: 2, 61 - 71, 14.06.2024
https://doi.org/10.46871/eams.1456762

Abstract

Ophthalmology, the medical field dedicated to eye care, is undergoing a transformation due to the advent of artificial intelligence (AI). This review article explores the growing use of AI in ophthalmic practices, focusing on disease diagnosis, screening, and surgical guidance. We examine the potential benefits of AI-powered tools, including their ability to improve the accuracy, efficiency, and accessibility of eye care. However, we also acknowledge the ethical and practical challenges associated with this technology, such as algorithmic bias, the lack of explainability, and potential job displacement. We envision a future where ophthalmologists and AI collaborate to improve patient care and usher in a new era of ophthalmic practice.

References

  • 1. Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Basingstoke). 2019;34(3):451-460. doi:10.1038/s41433-019-0566-0
  • 2. Yılmaz İE, Doğan L. Talking technology: exploring chatbots as a tool for cataract patient education. Clin Exp Optom. Published online 2024. doi:10.1080/08164622.2023.2298812
  • 3. Levent D, Bekir ÖG, Edhem YĬ. The Performance of Chatbots and the AAPOS Website as a Tool for Amblyopia Education. Journal of Pediatric Ophthalmology & Strabismus. 2024;0(0):1-7. doi:10.3928/01913913-20240409-01
  • 4. Rogers TW, Jaccard N, Carbonaro F, et al. Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study. Eye (Basingstoke). 2019;33(11):1791-1797. doi:10.1038/s41433-019-0510-3
  • 5. Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye (Basingstoke). 2018;32(6):1138-1144. doi:10.1038/s41433-018-0064-9
  • 6. Prashar J, Tay N. Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis. Eye. 2024;38(2):303-314. doi:10.1038/s41433-023-02680-z
  • 7. Huang Y, Cheung CY, Li D, et al. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye (Basingstoke). 2023;38(3):464-472. doi:10.1038/s41433-023-02724-4 8. Şenol A, Talan T, Aktürk C. A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis. Signal Image Video Process. 2024;18(5):4589-4603. doi:10.1007/S11760-024-03097-1/METRICS
  • 9. Srivastava O, Tennant M, Grewal P et al. Artificial intelligence and machine learning in ophthalmology: A review. Indian J Ophthalmol. 2023;71(1):11. doi:10.4103/IJO.IJO_1569_22
  • 10. He J, Cao T, Xu F, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye (Basingstoke). 2019;34(3):572-576. doi:10.1038/s41433-019-0562-4
  • 11. Rogers TW, Gonzalez-Bueno J, Garcia Franco R, et al. Evaluation of an AI system for the detection of diabetic retinopathy from images captured with a handheld portable fundus camera: the MAILOR AI study. Eye (Basingstoke). 2020;35(2):632-638. doi:10.1038/s41433-020-0927-8
  • 12. Cheung R, Chun J, Sheidow T et al. Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis. Eye (Basingstoke). 2021;36(5):994-1004. doi:10.1038/s41433-021-01540-y
  • 13. Crincoli E, Ferrara S, Miere A, et al. Correlation between AI-measured lacquer cracks extension and development of myopic choroidal neovascularization. Eye (Basingstoke). 2023;37(14):2963-2968. doi:10.1038/s41433-023-02451-w
  • 14. Korot E, Wood E, Weiner A et al. A renaissance of teleophthalmology through artificial intelligence. Eye (Basingstoke). 2019;33(6):861-863. doi:10.1038/s41433-018-0324-8
  • 15. Rajalakshmi R. The impact of artificial intelligence in screening for diabetic retinopathy in India. Eye (Basingstoke). 2019;34(3):420-421. doi:10.1038/s41433-019-0626-5
  • 16. Bahl A, Rao S. Diabetic retinopathy screening in rural India with portable fundus camera and artificial intelligence using eye mitra opticians from Essilor India. Eye (Basingstoke). 2020;36(1):230-231. doi:10.1038/s41433-020-01350-8
  • 17. Anguita R, Makuloluwa A, Hind J et al. Large language models in vitreoretinal surgery. Eye 2023 38:4. 2023;38(4):809-810. doi:10.1038/s41433-023-02751-1
  • 18. Poh SSJ, Sia JT, Yip MYT et al. Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases. Ophthalmol Retina. Published online January 26, 2024. doi:10.1016/J.ORET.2024.01.018
  • 19. Ates HC, Brunauer A, von Stetten F, et al. Integrated devices for non-invasive diagnostics. Adv Funct Mater. 2021;31(15):2010388. doi:10.1002/adfm.202010388
  • 20. Gambhir SS, Ge TJ, Vermesh O et al. Continuous health monitoring: an opportunity for precision health. Sci Transl Med. 2021;13(597):eabe5383. doi:10.1126/scitranslmed.abe5383
  • 21. Heikenfeld J, Jajack A, Rogers J, et al. Wearable sensors: modalities, challenges, and prospects. Lab Chip. 2018;18(2):217-248. doi:10.1039/c7lc00914c
  • 22. Iqbal SMA, Mahgoub I, Du E et al. Advances in healthcare wearable devices. npj Flex Electron. 2021;5(1). doi:10.1038/s41528-021-00107-x
  • 23. Zhang J, Kim K, Kim HJ, et al. Smart soft contact lenses for continuous 24-hour monitoring of intraocular pressure in glaucoma care. Nature Communications 2022 13:1. 2022;13(1):1-15. doi:10.1038/s41467-022-33254-4
  • 24. Amini P, Okeme J. Tear Fluid as a Matrix for Biomonitoring Environmental and Chemical Exposures. Published online December 6, 2023. doi:10.21203/RS.3.RS-3711147/V3
  • 25. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-2410. doi:10.1001/JAMA.2016.17216
  • 26. Xie Y, Nguyen QD, Hamzah H, et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health. 2020;2(5):e240-e249. doi:10.1016/S2589-7500(20)30060-1
  • 27. Yang K, Nambudiri VE. Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery. Appl Clin Inform. 2021;12(5):1157-1160. doi:10.1055/S-0041-1740259/ID/JR210140LE-27/BIB
  • 28. Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10 18.doi:10.1177/0840470419873123/ASSET/IMAGES/LARGE/10.1177_0840470419873123-FIG1.JPEG
  • 29. Bailey JE, Gurgol C, Pan E, et al. Early Patient-Centered Outcomes Research Experience with the Use of Telehealth to Address Disparities: Scoping Review. J Med Internet Res. 2021;23(12):e28503. doi:10.2196/28503
  • 30. Purcell WM, Burrell DN. Dynamic Evaluation Approaches to Telehealth Technologies and Artificial Intelligence (AI) Telemedicine Applications in Healthcare and Biotechnology Organizations. Merits 2023, Vol 3, Pages 700-721. 2023;3(4):700-721. doi:10.3390/MERITS3040042
  • 31. Wang C, Yao C, Chen P et al. Artificial Intelligence Algorithm with ICD Coding Technology Guided by Embedded Electronic Medical Record System in Medical Record Information Management. Microprocess Microsyst. Published online October 13, 2023:104962. doi:10.1016/J.MICPRO.2023.104962
  • 32. Young JA, Chang CW, Scales CW et al. Machine Learning Methods Using Artificial Intelligence Deployed on Electronic Health Record Data for Identification and Referral of At-Risk Patients From Primary Care Physicians to Eye Care Specialists: Retrospective, Case-Controlled Study. JMIR AI 2024;3:e48295 https://ai.jmir.org/2024/1/e48295. 2024;3(1):e48295. doi:10.2196/48295
  • 33. Bhuiyan A, Wong TY, Ting DSW et al. Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD. Transl Vis Sci Technol. 2020;9(2):25-25. doi:10.1167/TVST.9.2.25
  • 34. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 2019 25:1. 2019;25(1):44-56. doi:10.1038/S41591-018-0300-7
  • 35. Luxton DD. Ethical implications of conversational agents in global public health. Bull World Health Organ. 2020;98(4):285-287. doi:10.2471/BLT.19.237636
  • 36. Evans NG, Wenner DM, Cohen IG, et al. Emerging Ethical Considerations for the Use of Artificial Intelligence in Ophthalmology. Ophthalmology Science. 2022;2(2):100141. doi:10.1016/J.XOPS.2022.100141
  • 37. Nazerid LH, Zatarah R, Waldrip S, et al. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health.2023;2(6):e0000278. doi:10.1371/JOURNAL.PDIG.0000278
  • 38. Khunte M, Chae A, Wang R, et al. Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging. Clin Radiol. 2023;78(2):123-129. doi:10.1016/J.CRAD.2022.09.122
  • 39. Li H, Moon JT, Purkayastha S et al. Ethics of large language models in medicine and medical research. Lancet Digit Health. 2023;5(6):e333-e335. doi:10.1016/S2589-7500(23)00083-3
  • 40. Triberti S, Durosini I, Pravettoni G. A “Third Wheel” Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective. Front Public Health. 2020;8:517191.doi:10.3389/FPUBH.2020.00117/BIBTEX
  • 41. Durosini I, Pizzoli SFM, Strika M et al. Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context. Artificial Intelligence for Medicine. Published online January 1, 2024:231-237. doi:10.1016/B978-0-443-13671-9.00011-9
  • 42. Khan WU, Seto E. A “Do No Harm” Novel Safety Checklist and Research Approach to Determine Whether to Launch an Artificial Intelligence–Based Medical Technology: Introducing the Biological-Psychological, Economic, and Social (BPES) Framework. J Med Internet Res. 2023;25(1):e43386. doi:10.2196/43386
There are 41 citations in total.

Details

Primary Language English
Subjects Ophthalmology
Journal Section Review
Authors

İbrahim Edhem Yılmaz 0000-0003-1154-425X

Early Pub Date July 4, 2024
Publication Date June 14, 2024
Submission Date March 21, 2024
Acceptance Date June 7, 2024
Published in Issue Year 2024 Volume: 5 Issue: 2

Cite

Vancouver Yılmaz İE. The Rise of the Machines: Artificial Intelligence in Ophthalmology - A Boon or Bane?. Exp Appl Med Sci. 2024;5(2):61-7.

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