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Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.

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Titel: Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.
Autoren: Zeppieri, Marco, Gagliano, Caterina, Tognetto, Daniele, Musa, Mutali, Avitabile, Alessandro, D'Esposito, Fabiana, Nicolosi, Simonetta Gaia, Capobianco, Matteo
Quelle: Journal of Clinical Medicine; Sep2025, Vol. 14 Issue 18, p6519, 25p
Schlagwörter: GLAUCOMA, ARTIFICIAL intelligence, DEEP learning, ALGORITHMIC bias, DIAGNOSIS, HEALTH services accessibility, THERAPEUTICS, DECISION support systems
Abstract: Glaucoma remains a leading cause of irreversible blindness. We reviewed more than 150 peer-reviewed studies (January 2019–July 2025) that applied artificial or augmented intelligence (AI/AuI) to glaucoma care. Deep learning systems analyzing fundus photographs or OCT volumes routinely achieved area-under-the-curve values around 0.95 and matched—or exceeded—subspecialists in prospective tests. Sequence-aware models detected visual field worsening up to 1.7 years earlier than conventional linear trends, while a baseline multimodal network integrating OCT, visual field, and clinical data predicted the need for incisional surgery with AUROC 0.92. Offline smartphone triage in community clinics reached sensitivities near 94% and specificities between 86% and 94%, illustrating feasibility in low-resource settings. Large language models answered glaucoma case questions with specialist-level accuracy but still require human oversight. Key obstacles include algorithmic bias, workflow integration, and compliance with emerging regulations, such as the EU AI Act and FDA GMLP. With rigorous validation, bias auditing, and transparent change control, AI/AuI can augment—rather than replace—clinician expertise, enabling earlier intervention, tailored therapy, and more equitable access to glaucoma care worldwide. [ABSTRACT FROM AUTHOR]
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Datenbank: Biomedical Index
Beschreibung
Abstract:Glaucoma remains a leading cause of irreversible blindness. We reviewed more than 150 peer-reviewed studies (January 2019–July 2025) that applied artificial or augmented intelligence (AI/AuI) to glaucoma care. Deep learning systems analyzing fundus photographs or OCT volumes routinely achieved area-under-the-curve values around 0.95 and matched—or exceeded—subspecialists in prospective tests. Sequence-aware models detected visual field worsening up to 1.7 years earlier than conventional linear trends, while a baseline multimodal network integrating OCT, visual field, and clinical data predicted the need for incisional surgery with AUROC 0.92. Offline smartphone triage in community clinics reached sensitivities near 94% and specificities between 86% and 94%, illustrating feasibility in low-resource settings. Large language models answered glaucoma case questions with specialist-level accuracy but still require human oversight. Key obstacles include algorithmic bias, workflow integration, and compliance with emerging regulations, such as the EU AI Act and FDA GMLP. With rigorous validation, bias auditing, and transparent change control, AI/AuI can augment—rather than replace—clinician expertise, enabling earlier intervention, tailored therapy, and more equitable access to glaucoma care worldwide. [ABSTRACT FROM AUTHOR]
ISSN:20770383
DOI:10.3390/jcm14186519