12. Retinopathy, Neuropathy, and Foot Care: Standards of Medical Care in Diabetes—2022
Open accessAbstract
The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Prior to autonomous AI testing, all patients were referred to ophthalmology for an annual eye examination.
“Prior to autonomous AI testing, all patients were referred to ophthalmology for an annual eye examination.59”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [59]
Section 12.3 recommends eye examinations that are generally repeated annually and permits examination by an ophthalmologist or optometrist. It does not establish that, before autonomous AI testing, all patients were referred to ophthalmology for annual examinations; it also allows 1–2-year screening intervals for some patients. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAmidst rising DR prevalence, autonomous AI testing presents a promising solution to facilitate early detection, promote equitable access, and drive down system-level cost of care.
“Amidst rising DR prevalence, autonomous AI testing presents a promising solution to facilitate early detection, promote equitable access, and drive down system-level cost of care.42,48,67”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [42]
Section 12.3 identifies FDA-authorized AI systems as an alternative for diabetic-retinopathy screening and explains why prompt detection matters. It attributes improved access and possible cost savings to retinal photography with remote expert reading, not specifically to autonomous AI, and says the benefits and optimal use of AI screening remain undetermined. The cited passages do not establish rising retinopathy prevalence. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn the KDIGO and ADA algorithms, the diagnosis of moderately increased or severely increased albuminuria requires increased albumin excretion on two of three tests repeated over 3 to 6 months and exclusion of conditions that invalidate the test.
“In the KDIGO and ADA algorithms for urine albumin testing (506), the diagnosis of moderately increased or severely increased albuminuria requires the demonstration of increased albumin excretion on two of three tests repeated at intervals over a period of 3 to 6 months and exclusion of conditions that invalidate the test.”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [506]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredStanford University found that those referred after an AI-based examination followed up at the eye institute at a rate nearly 3 times higher than those referred after teleophthalmology-only testing.
“In the same study as previously mentioned, Stanford University found that those referred after an AI-based examination followed up at the eye institute at a rate nearly 3 times higher than those referred after teleophthalmology-only testing.42”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [42]
Section 12.3 discusses AI-based retinal screening and follow-up after abnormal results, but it does not report a Stanford study or compare follow-up rates after AI-based and teleophthalmology-only examinations. Searches for the claimed study and the nearly threefold follow-up difference found no matching passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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Diabetische Neuropathie und diabetischer Fuß (Update 2023)
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Vitamin B 12 and Diabetic Foot: Α Mini-Review
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- Authors
- American Diabetes Association Professional Practice Committee
- Corresponding author
- American Diabetes Association Professional Practice Committee
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2021-12-16
- Volume
- 45
- Issue
- Supplement_1
- Pages
- S185-S194
- ISSN
- 0149-5992 (ISSN-L)
- OpenAlex
- W4200289220
- Cited by
- 177
- References
- 95
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 40 · 2023: 60 · 2022: 41
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetic Foot Ulcer Assessment and Management
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy, diabetic neuropathy, diabetic foot care, diabetes complications, clinical guidelines, quality of care
- MeSH terms
- Diabetes Mellitus, Endocrinology, Humans, Reference Standards, Retinal Diseases, Societies, Medical, Standard of Care
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