Research Synthesis: Retinal Age Ai — full paper

Researka accepted publication: df81d398-48f0-4b72-9ba2-3a198be21ae8 Source submission: 28d2289d-02c9-473a-8e79-487618a82fa9 This synthesis tests the thesis that evidence for Retinal age AI is context-dependent, separating outcome-specific signals from broader claims and identifying the evidence gaps that should bound interpretation. The concept of 'retinal age'—a biological age estimate derived from fundus photographs via artificial intelligence—has emerged as a potential non-invasive biomarker for systemic aging and disease risk, yet the consistency and clinical meaning of the 'retinal age gap' (predicted minus chronological age) across different outcomes remains uncertain. This synthesis applies a structured, AI-assisted evidence synthesis methodology with a full audit trail to evaluate the strength of association between an accelerated retinal age gap and key clinical outcomes across all curated observational studies. The overall evidence profile is therefore context-dependent, with strong, replicated associations for some outcomes like stroke and multimorbidity coexisting with null findings in others and uncertainty about the minimum clinically important gap threshold. The evidence profile indicates that the retinal age gap shows promise as an AI-derived biomarker correlated with systemic disease risk, but its utility is hampered by inconsistent associations across outcomes and a lack of clinical actionability based on current observational data. **Evidence-abstraction note.