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TRUVACE RECORD VERSION
record: TRV-2026-1127
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-18T06:54:49.240749Z
status: published
lens: p_space
sector: health
headline: Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases
dek: Artificial intelligence (AI) has shown remarkable accuracy in the diagnosis of common ocular diseases such as diabetic retinopathy (DR), glaucoma, retinopathy of prematurity (ROP), and age-related macular degeneration (AMD), often matching or even outperforming expert clinicians. Despite these advancements, AI adoption in clinical settings remains limited due to key barriers. This systematic review evaluates 34 studies (2018-2025) highlighting AI's diagnostic performance (often >90% accuracy) while pointing out…
gain_title: (none)
problem_title: Despite high accuracy, AI fundus diagnostics saw limited clinical adoption due to poor generalizability and disjointed workflow integration.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Despite high accuracy, AI fundus diagnostics saw limited clinical adoption due to poor generalizability and disjointed workflow integration.
problem_evidence: AI adoption in clinical settings remains limited due to key barriers | poor generalizability across diverse populations | disjointed integration into clinical workflows
quick_read: A systematic review of 34 studies from 2018-2025 found AI systems for fundus image diagnosis of diabetic retinopathy, glaucoma, ROP and AMD frequently exceeded 90% accuracy and matched or outperformed experts, yet adoption in eye clinics remained limited.

The gap matters because high laboratory accuracy has not translated into routine vision care; the review attributes this to workflow integration failures, lack of transparency in decision-making, and poor generalizability, leaving questions about how to achieve equitable real-world deployment.
limitation: 
tag: Evidence-backed problem
key_points: Systematic review covered 34 studies from 2018-2025 on AI fundus diagnostics. | Reported AI accuracy often exceeded 90% for DR, glaucoma, ROP and AMD. | Authors identified three deployment gaps: workflow integration, transparency, and generalizability.
rundown: The review synthesized 34 studies published between 2018 and 2025, focusing on AI for fundus image diagnosis of DR, glaucoma, ROP and AMD.

It framed the challenge as the 'last-mile gap' between research and clinical practice and proposed pathways toward equitable, scalable and trustworthy implementation.
sources:
- peer_reviewed | International Journal of Ophthalmology | https://doi.org/10.18240/ijo.2026.09.17 | 2026-09-18
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