Introduction
Ocular Toxoplasmosis (OT) remains a major global cause of infectious posterior uveitis, yet its diagnosis continues to challenge clinicians due to overlapping retinal manifestations and dependence on expert image interpretation. As digital ophthalmology advances, artificial intelligence (AI), particularly deep learning, has emerged as a transformative tool for automating diagnostic workflows and reducing subjective variability. Recent research has increasingly focused on convolutional neural networks (CNNs) trained on fundus images to stratify OT from other retinochoroiditides. However, despite promising diagnostic performance metrics, the literature highlights significant gaps in dataset size, diversity, and external validation. This context underscores the need for systematic evaluation of AI-driven OT diagnosis and positions the current scoping review as a crucial step in mapping existing evidence, identifying methodological shortcomings, and guiding future research directions.
Current Landscape of AI Applications in OT Diagnosis
Research on AI-assisted OT detection is in an early but rapidly evolving stage, with most studies employing CNN-based models for binary classification tasks using standard fundus photography. Reported accuracies frequently exceed 87%, demonstrating clear technical potential. Yet, the evidence base is limited by the predominance of single-center datasets, varying imaging protocols, inconsistent ground truth labeling, and scarce evaluation against external populations. These constraints highlight the need for a more structured and harmonized research ecosystem to confirm AI’s role in real-world diagnosis of OT.
Dataset Limitations and Implications for Model Performance
A recurrent challenge across existing studies is the heavy reliance on small, imbalanced, and geographically narrow datasets. Such limitations introduce risks of overfitting, restrict generalizability, and inflate performance metrics during internal validation. The absence of multi-center data curation further prevents AI models from capturing the full phenotypic spectrum of OT and its mimickers. Addressing dataset deficiencies is essential for building clinically trustworthy AI systems capable of consistent performance across diverse clinical settings.
The Critical Role of External and Prospective Validation
A key barrier to translation lies in the near-universal lack of external validation, with many studies dependent solely on internal cross-validation. Without evaluation on independent datasets or prospective patient cohorts, diagnostic models risk failing when introduced into clinical workflows. External validation not only ensures robustness but also uncovers biases related to imaging devices, population characteristics, or labeling inconsistencies. Prospective clinical trials and real-world implementation studies remain vital next steps for establishing AI credibility in OT diagnosis.
Explainable AI (XAI) and the Need for Transparency
Despite rapid model development, explainability remains severely underrepresented in OT-focused AI research. Most existing models function as “black boxes,” providing predictions without interpretable reasoning that clinicians can trust. Implementing XAI—such as saliency maps, attention mechanisms, or feature attribution—would enhance transparency, support clinical acceptance, and facilitate error analysis. XAI is particularly crucial for OT, where differentiating subtle retinal features from other causes of posterior uveitis requires clarity and diagnostic accountability.
Future Research Directions and Clinical Translation Pathways
Progress toward clinically deployable AI tools for OT will require coordinated multi-center collaborations, standardized imaging protocols, and robust validation frameworks. Future research should expand beyond simple binary classification to address real-world diagnostic challenges, including multi-class differentiation of posterior uveitis entities. Additionally, integrating clinical metadata, leveraging multimodal imaging, and designing implementation-focused studies will be essential for bridging the translational divide. Ultimately, research must move toward reliable, transparent, and scalable AI systems capable of assisting ophthalmologists in the nuanced diagnosis of OT.
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