Clinical bottom line
Recent advances in artificial intelligence (AI) offer promising tools for predicting visual recovery in patients with central serous chorioretinopathy (CSC), a condition characterized by serous retinal detachment and variable visual dysfunction. AI models integrating multimodal imaging and clinical data may enhance prognostic accuracy beyond traditional clinical assessment, potentially guiding personalized management strategies. However, current evidence remains preliminary and largely hypothesis-generating, warranting cautious interpretation and further validation before routine clinical adoption.
What the evidence shows
Central serous chorioretinopathy is a complex retinal disorder with variable visual outcomes, influenced by factors such as disease chronicity, extent of retinal pigment epithelium (RPE) damage, and choroidal changes (Meyerle et al., 2019). Traditional prognostic indicators include symptom duration, leakage patterns on fluorescein angiography, and optical coherence tomography (OCT) features, but these have limited predictive precision.
Emerging AI approaches leverage deep learning algorithms trained on large datasets combining OCT, fundus autofluorescence, and clinical parameters to predict visual acuity recovery trajectories. These models can identify subtle imaging biomarkers and nonlinear interactions that may elude human interpretation. For example, AI-based analysis of retro-mode imaging, which enhances visualization of subretinal fluid and RPE alterations, has shown potential in characterizing disease severity and predicting outcomes (Maltsev et al., 2019).
Additionally, AI-driven risk stratification may assist in identifying patients likely to benefit from early intervention with oral agents such as mineralocorticoid receptor antagonists (e.g., spironolactone), which have demonstrated efficacy in promoting resolution and visual improvement in CSC (Fricella et al., 2025). Integrating AI predictions with treatment response data could refine personalized therapy decisions.
Caveats and uncertainty
Despite encouraging preliminary data, AI models for predicting visual recovery in CSC face several limitations. Most studies to date are retrospective, involve small cohorts, or are case series lacking external validation (Fricella et al., 2025). The heterogeneity of CSC phenotypes and variable imaging protocols challenge model generalizability.
Moreover, AI algorithms often operate as “black boxes,” limiting interpretability and clinician trust. The incremental benefit of AI predictions over experienced clinical judgment remains to be rigorously quantified. Importantly, AI tools do not replace comprehensive clinical evaluation but may serve as adjuncts.
Finally, the natural history of CSC includes spontaneous resolution in many cases, complicating the assessment of AI-driven prognostic accuracy. Longitudinal prospective studies with standardized imaging and functional endpoints are needed to establish clinical utility.
How this may change practice
If validated, AI-based predictive models could enhance ophthalmologists’ ability to counsel CSC patients regarding expected visual recovery timelines and likelihood of persistent dysfunction. This may facilitate earlier identification of cases warranting intervention versus observation, optimizing resource allocation and patient outcomes.
Integration of AI predictions into electronic health records and imaging platforms could streamline risk stratification workflows. Furthermore, AI may support personalized treatment planning by predicting responsiveness to emerging therapies such as mineralocorticoid receptor antagonists.
Clinicians should remain informed about ongoing developments but apply AI tools judiciously, recognizing current evidence limitations. Multidisciplinary collaboration between clinicians, imaging specialists, and data scientists will be essential to translate AI advances into safe, effective clinical decision support.