Harnessing AI for Predictive and Personalized Eye Health

AI is reshaping how clinicians anticipate and manage drug-induced retinal toxicity, offering new tools that complement established screening practices while moving ophthalmic care toward greater personalization.

Retinal safety remains a central concern for patients on long-term systemic therapies such as hydroxychloroquine. AI approaches are increasingly explored as adjuncts for predicting retinal toxicity—particularly with hydroxychloroquine—while clinicians continue to rely on AAO screening guidelines (2016/2020 updates).

Such innovations point toward enhanced care. At a technical level, advances in computer vision have focused on retinal image analysis, where convolutional neural networks (CNNs) learn subtle patterns associated with early toxicity. By analyzing complex retinal imaging data, CNNs have shown high accuracy in early detection in retrospective datasets, avoiding the need to claim superior benchmarks outright.

In one line of research, CNN-based systems trained on multimodal retinal images have been evaluated for medication-induced ocular damage, providing decision support that can cue earlier confirmatory testing. While promising, performance typically reflects retrospective datasets and requires prospective validation and calibration before routine deployment.

Personalization is an equally important thread. Virtual eye models—computational biophysical models that simulate individual ocular anatomy and optics—are at the forefront of this personalization, simulating patient-specific characteristics to tailor treatments effectively. Digital twins—specific implementations of virtual eye models that are continuously updated with patient data—enhance these capabilities by providing dynamic, patient-specific simulations to aid in diagnostics and tailor treatments further.

For patients with drug-induced retinal toxicity—such as those on hydroxychloroquine—or symptoms like scotomas, AI tools can help stratify risk and tailor monitoring and follow-up. Early pilot programs and local clinical pathways are exploring integration of AI-enabled virtual eye platforms to support more individualized care. In practice, this could mean prioritizing higher-risk patients for earlier OCT or visual field testing based on model-estimated risk.

Yet, managing drug-induced retinal toxicity remains challenging—data heterogeneity, device variability, and workflow integration still limit broad deployment. Algorithm performance can vary across imaging modalities and clinic settings, and prospective validation across diverse populations is essential to confirm generalizability. Clear handoffs between automated flags and clinician decision-making also need to be defined to avoid alert fatigue and ensure guideline-concordant care.

Advances in AI modeling now allow for more actionable predictive insights—for example, flagging patients for earlier OCT screening when risk thresholds are exceeded. To responsibly translate these insights, teams should align outputs with AAO-recommended screening intervals, account for dose and duration risk factors, and document how AI outputs influence follow-up or treatment plans. Transparency, calibration monitoring, and periodic revalidation are key safeguards as models encounter shifting populations and devices.

Ethical and equity considerations run alongside technical challenges. Bias can emerge if training data underrepresent certain ages, ethnicities, or comorbidities, potentially widening disparities in detection. Mitigations include dataset audits, performance stratification by subgroup, and conservative deployment with human-in-the-loop review—particularly when AI suggestions deviate from guideline-based pathways.

Looking ahead, the most effective implementations will likely blend imaging-based risk estimation with medication exposure data (dose, duration, renal function) and patient-reported symptoms to refine who needs intensified surveillance. As these systems mature, the goal is not to replace guideline-driven care but to support it—surfacing at-risk patients earlier, standardizing image interpretation, and freeing clinicians to focus on nuanced decision-making and patient counseling.

Key Takeaways:

• AI’s pattern recognition may enable earlier detection of hydroxychloroquine-related retinal toxicity when used alongside guideline-recommended screening and risk stratification by dose and duration.
• CNN-based image analysis shows promise in retrospective datasets for early detection of medication-related ocular changes; prospective validation is needed.
• Virtual eye models and their data-updated counterparts, digital twins, offer pathways to personalize care planning and monitoring.
• Implementation barriers—data heterogeneity, device variability, and workflow fit—remain active challenges; human oversight and guideline alignment are essential.