William Seiple, Hilde P A van der Aa, Fernanda Garcia-Piña, Izekiel Greco, Calvin Roberts, Ruth van Nispen
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Usability was evaluated with the System Usability Scale (SUS).</p><p><strong>Results: </strong>The odds ratios (ORs) of being able to complete Text tasks were significantly higher when using AAIIs compared to the baseline. OR when performing \"Searching and Identifying\" tasks varied among AAIIs, with Seeing AI and Envision improving the performance of more tasks than Lookout or OrCam. Participants expressed high satisfaction with the AAIIs.</p><p><strong>Conclusions: </strong>Despite the findings that performance on some tasks and when using some AAIIs did not result in a greater number of PVL being able to complete the tasks, there was overall high satisfaction, reflecting an acceptance of AI as an assistive technology and the promise of this developing technology.</p><p><strong>Translational relevance: </strong>This evidence-based performance data provide guidelines for clinicians when recommending an AAII to PVL.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 1","pages":"3"},"PeriodicalIF":2.6000,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721483/pdf/","citationCount":"0","resultStr":"{\"title\":\"Performance on Activities of Daily Living and User Experience When Using Artificial Intelligence by Individuals With Vision Impairment.\",\"authors\":\"William Seiple, Hilde P A van der Aa, Fernanda Garcia-Piña, Izekiel Greco, Calvin Roberts, Ruth van Nispen\",\"doi\":\"10.1167/tvst.14.1.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study assessed objective performance, usability, and acceptance of artificial intelligence (AI) by people with vision impairment. The goal was to provide evidence-based data to enhance technology selection for people with vision loss (PVL) based on their loss and needs.</p><p><strong>Methods: </strong>Using a cross-sectional, counterbalanced, cross-over study involving 25 PVL, we compared performance using two smart glasses (OrCam and Envision Glasses) and two AI apps (Seeing AI and Google Lookout). We refer to these as assistive artificial intelligence implementations (AAIIs). Completion and timing were quantified for three task categories: text, text in columns, and searching and identifying. Usability was evaluated with the System Usability Scale (SUS).</p><p><strong>Results: </strong>The odds ratios (ORs) of being able to complete Text tasks were significantly higher when using AAIIs compared to the baseline. OR when performing \\\"Searching and Identifying\\\" tasks varied among AAIIs, with Seeing AI and Envision improving the performance of more tasks than Lookout or OrCam. 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Performance on Activities of Daily Living and User Experience When Using Artificial Intelligence by Individuals With Vision Impairment.
Purpose: This study assessed objective performance, usability, and acceptance of artificial intelligence (AI) by people with vision impairment. The goal was to provide evidence-based data to enhance technology selection for people with vision loss (PVL) based on their loss and needs.
Methods: Using a cross-sectional, counterbalanced, cross-over study involving 25 PVL, we compared performance using two smart glasses (OrCam and Envision Glasses) and two AI apps (Seeing AI and Google Lookout). We refer to these as assistive artificial intelligence implementations (AAIIs). Completion and timing were quantified for three task categories: text, text in columns, and searching and identifying. Usability was evaluated with the System Usability Scale (SUS).
Results: The odds ratios (ORs) of being able to complete Text tasks were significantly higher when using AAIIs compared to the baseline. OR when performing "Searching and Identifying" tasks varied among AAIIs, with Seeing AI and Envision improving the performance of more tasks than Lookout or OrCam. Participants expressed high satisfaction with the AAIIs.
Conclusions: Despite the findings that performance on some tasks and when using some AAIIs did not result in a greater number of PVL being able to complete the tasks, there was overall high satisfaction, reflecting an acceptance of AI as an assistive technology and the promise of this developing technology.
Translational relevance: This evidence-based performance data provide guidelines for clinicians when recommending an AAII to PVL.
期刊介绍：
Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO.
The journal covers a broad spectrum of work, including but not limited to:
Applications of stem cell technology for regenerative medicine,
Development of new animal models of human diseases,
Tissue bioengineering,
Chemical engineering to improve virus-based gene delivery,
Nanotechnology for drug delivery,
Design and synthesis of artificial extracellular matrices,
Development of a true microsurgical operating environment,
Refining data analysis algorithms to improve in vivo imaging technology,
Results of Phase 1 clinical trials,
Reverse translational ("bedside to bench") research.
TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.
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