Literature DB >> 34087473

Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology.

Nita G Valikodath1, Tala Al-Khaled1, Emily Cole1, Daniel S W Ting2, Elmer Y Tu1, J Peter Campbell3, Michael F Chiang4, Joelle A Hallak1, R V Paul Chan5.   

Abstract

PURPOSE: To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology.
METHODS: This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics.
RESULTS: A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI.
CONCLUSIONS: Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.
Copyright © 2021 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.

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Year:  2021        PMID: 34087473      PMCID: PMC8328946          DOI: 10.1016/j.jaapos.2021.01.011

Source DB:  PubMed          Journal:  J AAPOS        ISSN: 1091-8531            Impact factor:   1.325


  25 in total

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2.  Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

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3.  Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma.

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Journal:  Br J Ophthalmol       Date:  2014-05-02       Impact factor: 4.638

Review 4.  Big data and black-box medical algorithms.

Authors:  W Nicholson Price
Journal:  Sci Transl Med       Date:  2018-12-12       Impact factor: 17.956

5.  Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence imaging.

Authors:  Abbas Habibalahi; Chandra Bala; Alexandra Allende; Ayad G Anwer; Ewa M Goldys
Journal:  Ocul Surf       Date:  2019-03-20       Impact factor: 5.033

6.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

Authors:  Ziad Obermeyer; Ezekiel J Emanuel
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7.  Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

Authors:  Michael David Abràmoff; Yiyue Lou; Ali Erginay; Warren Clarida; Ryan Amelon; James C Folk; Meindert Niemeijer
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8.  Diagnostic Efficacy and Therapeutic Decision-making Capacity of an Artificial Intelligence Platform for Childhood Cataracts in Eye Clinics: A Multicentre Randomized Controlled Trial.

Authors:  Haotian Lin; Ruiyang Li; Zhenzhen Liu; Jingjing Chen; Yahan Yang; Hui Chen; Zhuoling Lin; Weiyi Lai; Erping Long; Xiaohang Wu; Duoru Lin; Yi Zhu; Chuan Chen; Dongxuan Wu; Tongyong Yu; Qianzhong Cao; Xiaoyan Li; Jing Li; Wangting Li; Jinghui Wang; Mingmin Yang; Huiling Hu; Li Zhang; Yang Yu; Xuelan Chen; Jianmin Hu; Ke Zhu; Shuhong Jiang; Yalin Huang; Gang Tan; Jialing Huang; Xiaoming Lin; Xinyu Zhang; Lixia Luo; Yuhua Liu; Xialin Liu; Bing Cheng; Danying Zheng; Mingxing Wu; Weirong Chen; Yizhi Liu
Journal:  EClinicalMedicine       Date:  2019-03-17

9.  Physician perspectives on integration of artificial intelligence into diagnostic pathology.

Authors:  Shihab Sarwar; Anglin Dent; Kevin Faust; Maxime Richer; Ugljesa Djuric; Randy Van Ommeren; Phedias Diamandis
Journal:  NPJ Digit Med       Date:  2019-04-26

Review 10.  Artificial intelligence and deep learning in ophthalmology.

Authors:  Daniel Shu Wei Ting; Louis R Pasquale; Lily Peng; John Peter Campbell; Aaron Y Lee; Rajiv Raman; Gavin Siew Wei Tan; Leopold Schmetterer; Pearse A Keane; Tien Yin Wong
Journal:  Br J Ophthalmol       Date:  2018-10-25       Impact factor: 4.638

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Journal:  Front Med (Lausanne)       Date:  2022-08-31

2.  Deepfakes in Ophthalmology: Applications and Realism of Synthetic Retinal Images from Generative Adversarial Networks.

Authors:  Jimmy S Chen; Aaron S Coyner; R V Paul Chan; M Elizabeth Hartnett; Darius M Moshfeghi; Leah A Owen; Jayashree Kalpathy-Cramer; Michael F Chiang; J Peter Campbell
Journal:  Ophthalmol Sci       Date:  2021-11-16

3.  Artificial intelligence in (gastrointestinal) healthcare: patients' and physicians' perspectives.

Authors:  Quirine E W van der Zander; Mirjam C M van der Ende-van Loon; Janneke M M Janssen; Bjorn Winkens; Fons van der Sommen; Ad A M Masclee; Erik J Schoon
Journal:  Sci Rep       Date:  2022-10-06       Impact factor: 4.996

  3 in total

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