Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient
Open accessAbstract
Artificial intelligence (AI), or the use of automated systems that display the ability to correctly interpret, to learn from, and to achieve specific goals by use of external data, is an emerging technology that has myriad implications for changing the way we interact with the world. Although this technology is already being used in many fields such as banking, retail, and education, AI has the potential to transform other fields including healthcare. Within healthcare, ophthalmology is uniquely positioned to benefit from AI not only through clinical decision support technology but also through improved image processing innovations such as real-time segmentation, automated image quality improvements, and assisted or autonomous disease screening tools.1,2 Although there are now Food and Drug Administration–approved technologies within ophthalmology, such as IDx-DR (Coralville, IA, USA) for early diagnosis of diabetic retinopathy and diabetic macular edema, numerous challenges still exist to realize the potentially transformative impact of these technologies in day to day practice. The need for the ophthalmology community to take a thoughtful approach to AI innovation and implementation is accentuated by the high stakes involved. The impact of misleading patients and clinicians on a health condition is much greater than a retail store misinterpreting the next book you may like to buy. As a result, we need to increase discussion about the issues surrounding who, what, when, how, and why we might use AI in practice, including ethical and liability considerations, to determine how best to implement AI for all stakeholders including practitioners, patients, practices/hospitals, and industry. This article aims to highlight the challenges and barriers to real-world AI adoption that impact the technology's utility. We examine these specific challenges that will face health care organizations, providers, and patients.
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Human trust is an important factor, and further education on AI and transparency information can build this trust for clinicians and patients.
“Human trust is an important factor, and further education on AI and transparency information can build this trust for clinicians and patients [59–61].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [60]
The ‘Will Patients Embrace This Technology?’ section identifies trust in data security as a factor in patients’ acceptance of AI. The ‘Building an AI Competent Physician Workforce’ section calls for AI education for clinicians, and ‘Considerations for Adopting Existing AI Technology’ discusses more explainable AI. The paper does not say that education or transparency information builds trust for both clinicians and patients. (LLM, conf=0.84)
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- Authors
- Singh, R. P., Hom, G. L., Abramoff, M. D., Campbell, J. P., Chiang, M. F., & on behalf of the AAO Task Force on Artificial Intelligence
- Affiliations
- Cleveland Clinic (US)
- Case Western Reserve University (US)
- Retina Service, Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA
- Oregon Health & Science University (US)
- Corresponding author
- Singh, Rishi P.
- Venue
- Translational Vision Science & Technology
- Published
- 2020-08-11
- Volume
- 9
- Issue
- 2
- Pages
- 45
- ISSN
- 2164-2591 (ISSN-L)
- PMC
- PMC7443115
- OpenAlex
- W3048871739
- Cited by
- 259
- References
- 4
- Citations by year
- 2026: 43 · 2025: 65 · 2024: 75 · 2023: 36 · 2022: 25 · 2021: 13 · 2020: 2
- FWCI
- 5.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Translational Vision Science & Technology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (R01EY019474, K12EY027720, P30EY010572)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD107493)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- artificial intelligence adoption, ophthalmology, clinical decision support, diabetic retinopathy screening, AI implementation barriers, AI ethics, liability, patient trust, automated image analysis, real-world implementation
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Humans
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