ArticleOphthalmology science2026
Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Posterior segment imaging clues to systemic malignancy: a phenotype-to-confirmation narrative review.Frontiers in ophthalmology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This article explores the application of artificial intelligence (AI) in the differentiation of choroidal melanocytic lesions, specifically choroidal nevi and small melanomas, within the field of ocular oncology. The primary topic highlights the significance of accurately diagnosing these lesions to enhance patient outcomes and management strategies. Design: The study reviews of the role of AI in differentiating choroidal melanocytic lesions, particularly choroidal nevi from small melanomas, examining clinical and imaging risk factors. It explores deep learning (DL) applications for image classification and assesses AI's potential impact on patient care, diagnostic accuracy, and regulatory concerns in ocular oncology. Methods: To achieve this, the methods discussed in this paper revolve around employing DL techniques, which utilize artificial neural networks to analyze high-dimensional medical images. This approach enables automated classification and image analysis of ophthalmic data, allowing for the identification of intricate patterns and features that may be imperceptible to clinicians. Additionally, the text reviews existing clinical and imaging risk factors associated with the growth of choroidal nevi into melanoma, leveraging this information to inform and enhance AI algorithms. Results: The anticipated results of integrating AI into clinical practice include increased diagnostic accuracy, which can lead to earlier identification of high-risk lesions and, consequently, timely interventions. This proactive approach has the potential to improve patient care significantly by facilitating better management strategies, thus enhancing patient outcomes. Artificial intelligence may also uncover subtle imaging features that would otherwise be overlooked, providing a more comprehensive assessment of lesions. Conclusion: In conclusion, the paper emphasizes the transformative potential of AI in ocular oncology, advocating for its integration with existing imaging technologies. While AI offers promising advancements in diagnostic practices and patient care, the paper also acknowledges the necessity of addressing regulatory and implementation challenges to fully harness these benefits. Overall, the incorporation of AI technologies into the diagnostic workflow has the potential to not only save vision but also improve survival rates, marking a significant step forward in the management of choroidal melanocytic lesions. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.