Diagnostic Accuracy of an AI-Assisted System in Retinal Screening in Cancer Care: A Cureus Study Reveals
A recent retrospective cross-sectional observational study concluded that the Medios AI system, in conjunction with the Remidio smartphone-based fundus camera, has strong potential to detect common retinal pathologies, such as diabetic retinopathy (DR), age-related macular degeneration (ARMD), and glaucoma, in primary or screening settings.
However, the same study identified potential limitations of the system, including a tendency to misclassify leukemic retinopathy as diabetic retinopathy, a failure to detect optic atrophy, and an inability to capture peripheral retinal lesions due to hardware constraints.
This retrospective cross-sectional observational study was published in December 2025 in Cureus.
Introduction
Artificial intelligence (AI) is revolutionizing medical diagnostics by introducing tools that promise faster, more accessible, and often cost-effective solutions for disease detection and treatment. It has recently been integrated into ophthalmic diagnostics for screening retinal diseases. The Medios AI system by Remidio (Singapore, Singapore) detects diabetic retinopathy (DR), age-related macular degeneration (ARMD), and glaucoma; however, its performance in ocular oncology remains underexplored. Ocular oncology involves intraocular tumors, paraneoplastic syndromes, and systemic malignancies with ocular manifestations. Retinal findings can include leukemic retinopathy, choroidal metastases, optic nerve atrophy, and hemorrhagic retinopathy, which may present subtly or overlap.
Study Overview
A retrospective cross-sectional study was conducted at the National Cancer Institute, All India Institute of Medical Sciences, New Delhi, to assess the diagnostic performance of the Medios AI system in patients with ocular cancer. The study enrolled 98 patients (196 eyes) with systemic malignancies, including those undergoing treatment for leukaemia, lymphoma, and other tumours, who presented with ocular complaints. Patients with corneal ulcers, significant cataract, vitreous haemorrhage preventing fundus imaging, and those who were uncooperative were excluded. Fundus images were captured with a smartphone-based camera (Remidio) under standard lighting. Both the macular and optic disc regions were imaged, with peripheral retina when possible. The primary outcome was diagnostic concordance between the AI-generated findings and clinical diagnoses. Secondary outcomes included false positives, false negatives, misclassification analysis, and imaging system limitations.
Key Findings
Diagnostic performance of the Medios AI system
The AI identified glaucomatous cupping in three patients, confirmed by clinical exam, visual field tests, and OCT, showing optic disc ratios >0.7 and rim thinning. It flagged 10 cases of diabetic retinopathy, but only 2 were confirmed; 8 patients had leukemic retinopathy with similar retinal features, suggesting low specificity. The AI detected early-to-intermediate dry ARMD in two patients, with drusen and RPE irregularities. It misclassified 8 cases of leukemic retinopathy as DR, indicating difficulty distinguishing similar vascular retinopathies. The AI missed optic atrophy in 5 patients despite clinical signs of optic disc pallor, which is concerning given the importance of optic nerve issues in oncology patients with CNS complications (Table 1).
Table 1: Diagnostic performance of Medios AI system
| Retinal Condition | Cases Detected by AI | Confirmed Clinically | Correct Diagnoses | Misclassified / Missed Findings | % Missed |
| Glaucoma (Cupping) | 3 | 3 | 3 | None | 0% |
| Diabetic Retinopathy | 10 | 2 | 2 | 8 cases of leukemic retinopathy misclassified | 80% |
| Age-Related Macular Degeneration (ARMD) | 2 | 2 | 2 | None | 0% |
| Leukemic Retinopathy | 0 (all misclassified) | 8 | 0 | All 8 labelled as diabetic retinopathy by AI | 100% |
| Optic Atrophy | 0 | 5 | 0 | 5 completely missed | 100% |
| Peripheral Retinal Lesions (e.g., metastases, infiltrates) | Not detectable (hardware limitation) | Several noted clinically | 0 | Missed due to lack of wide-field/montage imaging | Not applicable |
Clinical Implications
Medios AI system shows potential for identifying prevalent retinal pathologies, such as diabetic retinopathy (DR), age-related macular degeneration (ARMD), and glaucoma; however, its limitations are pronounced in oncology. The system's inability to differentiate between similar haemorrhagic retinopathies, failure to detect optic nerve atrophy, and lack of wide-field imaging capabilities highlight the need for cautious implementation. However, the integration of AI tools must be accompanied by expert clinical oversight, particularly in specialised settings where retinal presentations are complex and atypical.
Reference: Das D, Chawla B, Lomi N, et al. AI in the Shadows: Unveiling the Strengths and Blind Spots of Medios AI Retinal Screening in Cancer Care. Cureus 17(12): e99002. Published December 11, 2025. DOI 10.7759/cureus.99002
