Determinants of the implementation of artificial intelligence-based screening for diabetic retinopathy—a cross-sectional study with general practitioners in Germany
Open accessAbstract
Objective: Diabetic retinopathy (DR) may lead to irreversible damage to the eye and cause blindness if diagnosed in its advanced stages. Artificial intelligence (AI) may support screening and contribute to a timely diagnosis. The aim of this study was to evaluate factors that might influence the success of implementing AI-supported devices for DR screenings in general practice. Methods: A questionnaire with modules on attitudes toward digital solutions, technical factors, perceived patient perspectives, and sociodemographic data was constructed and 2100 general practitioners (GPs) in Germany were invited to participate via a personal letter. Results: Two hundred nine physicians participated in the survey (10% response rate, mean age = 54 years, 46% women). Acquisition costs (mean = 1.37), remuneration (mean = 1.46), and running costs (mean = 1.40) were considered particularly relevant in the context of AI-based screening tools. GPs indicated that a mean of €27.00 (SD = 19) was considered to be an appropriate reimbursement for an AI-based screening for DR in their practice. Less relevant factors were availability of a smartphone used in the practice (mean = 2.53) and time until the examination result was available (mean = 2.29). Important technical factors were practicability of the device (mean = 1.27), unproblematic installation of any necessary software (mean = 1.34), and the integrability into the practice information system (mean = 1.44). Considering the patient welfare, physicians rated the accuracy of the examination, omission of pupil dilation, and the duration of the examination as the most important factors. Participants ranked the factors broadening the scope of care, strengthening the primary care (PC) range, and signs of modern medical practice as the most important factors for making an AI-based screening tool attractive for their practice. Conclusions: These findings serve as a basis for a successful implementation of AI-assisted screening devices in PC and might facilitate early screenings for ophthalmological diseases in general practice. The most relevant barriers that need to be overcome for a successful implementation of such tools include clarification of the costs and reimbursement policies.
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Doubts about reliability and accuracy negatively impact physician confidence in the AI system.
“Finally, doubts about reliability and accuracy may negatively impact physician confidence in the system [56,57].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
The Results (Table 1) identify examination validity as an important factor in implementing AI screening, and the Discussion reports physicians’ fear that the system could overlook other eye diseases. These passages support concern about accuracy, but the paper does not establish that such doubts reduce physicians’ confidence in the system; its Limitations also caution against causal conclusions. (LLM, conf=0.78)
Experimental — accuracy not yet measuredLack of integration of the AI system into the hospital/facility information system is a barrier in the inner setting domain.
“Also, the lack of integration of the AI system into the hospital/facility information system is another relevant concern that acts as a barrier [54,55,56].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
The Results (Technical Implementation, Table 2) rate integration into the practice information system as a relevant factor, and the Discussion calls integration into the practice system a technical concern. The study concerns German general practices, not hospital information systems, and these passages do not classify the concern under an “inner setting” domain. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredHigh costs of AI software/acquisition are a barrier to AI adoption in diabetes management.
“Another common barrier is related to the financial burden of AI software/high acquisition costs [52,53,56].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
The Conclusion identifies financial concerns about purchasing, implementing, and maintaining AI-assisted diabetic-retinopathy screening devices as the most relevant barrier reported by the surveyed German GPs. This supports the high-acquisition-cost point, but the paper does not specifically identify AI software costs or establish the same barrier across diabetes management more broadly. (LLM, conf=0.83)
Experimental — accuracy not yet measuredDiagnostic limitations of AI systems (they may not detect other conditions besides the one for which they have been designed) are a barrier to AI adoption.
“whereas the studies of Wewetzer et al. [56] and Wahlich et al. [57] highlighted diagnostic limitations of AI systems (they may not detect other conditions besides the one for which they have been designed for, leading to incomplete diagnosis).”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
Insufficient reimbursement by health care systems is a barrier in the outer setting domain.
“A study also reported that insufficient reimbursement by health care systems may act as a significant barrier [56].”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
Time-saving is a facilitator to AI adoption in diabetes management.
“the time sparing effect, both for the patient and for the physician [53,56,57]”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
Two quantitative cross-sectional studies were based on surveys.
“Two quantitative cross-sectional studies were based on surveys [54,56]”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
Wewetzer et al. was reported as medium-quality owing to the lack of managing confounding factors.
“while the study by Wewetzer et al. [56] was reported as medium-quality owing to the lack of managing confounding factors.”
From Barriers and Facilitators to Artificial Intelligence Implementation in Diabetes Management from Healthcare Workers’ Perspective: A Scoping Review (2025) · cites this paper as [56]
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Evaluation of AI tool assisting primary healthcare physicians to diagnostic and treatment tasks
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- Authors
- Wewetzer, L., Held, L. A., Goetz, K., & Steinhäuser, J.
- Affiliations
- University Hospital Schleswig-Holstein (DE)
- Corresponding author
- Wewetzer, Larisa
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2023-01
- Volume
- 9
- Pages
- 20552076231176644-20552076231176644
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC10233602
- OpenAlex
- W4379375837
- Cited by
- 23
- References
- 35
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 5
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Joachim Herz Stiftung
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- diabetic retinopathy, artificial intelligence, AI-based screening, implementation barriers, general practitioners, primary care, reimbursement, acquisition cost, device integration, patient well-being, digital health adoption, ophthalmic screening
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