Clinical bottom line
Artificial intelligence (AI) models are emerging as promising tools to predict visual outcomes after ocular chemical injuries, a clinical scenario historically challenging due to variable injury severity and healing responses. Early evidence suggests that AI-driven predictive algorithms, integrating clinical and imaging data, may improve prognostication accuracy beyond traditional clinical grading systems. However, current models remain preliminary, with limited external validation and heterogeneous input parameters. Clinicians should view AI predictions as adjunctive, hypothesis-generating aids that warrant further validation before routine clinical adoption.
What the evidence shows
Ocular chemical injuries cause significant visual morbidity with outcomes influenced by injury severity, agent type, and timely management (Waghmare et al., 2022; Hall et al., 2010). Traditional prognostic approaches rely on clinical grading scales such as the Roper-Hall or Dua classifications, which correlate injury extent with visual prognosis but have limitations in precision and individualized prediction.
Recent advances in AI have introduced machine learning models capable of analyzing complex datasets—including clinical features, slit-lamp images, and anterior segment optical coherence tomography (OCT)—to predict visual outcomes more accurately. For example, OrthoMedia (2024) reported AI algorithms that integrate multiple clinical variables to estimate surgical success and visual prognosis in ocular trauma, including chemical injuries. These models demonstrated improved predictive accuracy compared to conventional scoring methods, potentially enabling earlier identification of patients at high risk for poor outcomes.
Complementing AI prognostication, stem cell transplantation techniques for ocular surface reconstruction after chemical burns have shown promising visual rehabilitation outcomes (Sangwan et al., 2023). AI tools may help stratify candidates for such advanced therapies by predicting which patients are unlikely to recover adequate vision with standard care alone.
Despite these advances, the literature remains limited in large-scale prospective validation of AI models specifically for ocular chemical injuries. Most studies are retrospective, single-center, or extrapolated from broader ocular trauma datasets (Gursoy et al., 2017; Waghmare et al., 2022). Furthermore, heterogeneity in injury mechanisms, treatment protocols, and outcome measures complicates model generalizability.
Caveats and uncertainty
AI-based prediction in ocular chemical injuries is an evolving field with several caveats:
- Data heterogeneity: Variability in injury severity, chemical agents, and treatment timing across studies limits the consistency of input data used for AI training (Waghmare et al., 2022; Hall et al., 2010).
- Limited external validation: Most AI models have been developed and tested in single institutions or small cohorts, raising concerns about overfitting and applicability to diverse patient populations (OrthoMedia, 2024).
- Outcome measures: Visual acuity alone may not capture functional vision or quality of life, and standardized endpoints are lacking across studies (Das et al., 2026).
- Integration challenges: Incorporating AI predictions into clinical workflows requires user-friendly interfaces and clinician training, which are currently underdeveloped.
- Ethical and legal considerations: Reliance on AI for prognostication must be balanced with clinician judgment to avoid overdependence or misinterpretation.
How this may change practice
If validated and integrated effectively, AI prediction models could transform management of ocular chemical injuries by:
- Enhancing early prognostication: More precise risk stratification at presentation may guide intensity of monitoring, timing of surgical interventions, and patient counseling.
- Personalizing treatment: Identifying patients likely to benefit from advanced therapies such as stem cell transplantation or amniotic membrane grafting could optimize resource allocation.
- Improving clinical trial design: AI tools may help select homogeneous patient cohorts with predicted poor outcomes for novel therapeutic trials.
- Supporting teleophthalmology: Remote AI-assisted assessment could facilitate triage and early referral in resource-limited settings.
However, clinicians should apply AI predictions cautiously, considering them as complementary to comprehensive clinical evaluation rather than definitive forecasts.