ReviewOphthalmology and therapy2026
Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.
Review in Ophthalmology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Authors and funding
4 authors.
Funding
Abstract
Smartphone-based portable slit lamps are rapidly evolving from simple add-ons into practical, low-cost front-line tools for anterior segment care beyond traditional clinical settings. By integrating high-performance smartphone cameras with compact optical attachments (e.g., slit-light converters, macro lenses, blue filters) and dedicated applications, these devices deliver slit-lamp-style imaging and video capture to environments where conventional biomicroscopes are inaccessible. Accumulating clinical evidence-most notably for the Smart Eye Camera (SEC) and iSpector MINI HE 010-21-confirms that smartphone slit lamps reliably support assessments across major anterior segment disorders. SEC-based recordings enable evaluation of tear film-related signs in dry eye disease; smartphone-acquired slit-lamp images show strong agreement with standard slit lamps for corneal ulcers, scars, and other ocular surface pathologies; slit-beam acquisition facilitates preliminary screening of shallow anterior chambers and narrow angles relevant to primary angle-closure glaucoma; and cataract screening and grading via smartphone systems align closely with conventional slit-lamp evaluations. Notably, recent advancements have transcended mere image capture to embrace artificial intelligence (AI)-enabled analysis, positioning smartphone slit lamps as scalable screening and triage solutions. Across the studies reviewed, AI models trained on smartphone slit-lamp images or videos demonstrate robust feasibility for automated dry eye diagnosis, corneal opacity detection, keratitis screening, cataract grading, pterygium detection/grading, and narrow-angle identification, typically through pipelines integrating image quality control, region-of-interest localization/segmentation, and disease-specific prediction. Despite these advances, however, key barriers remain, including incomplete replication of full slit-lamp functionality, lack of standardized acquisition protocols, and limited multicenter external validation for most AI systems. Future progress should prioritize hardware stabilization, optical design improvements, disease-specific standardized imaging workflows, and large-scale prospective validation to unlock the full potential of AI-assisted smartphone slit lamps for community screening, teleophthalmology, and care in underserved regions.
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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 Socratic graph.