Public articles linked to the same research event.
International journal of ophthalmology This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.
This systematic review evaluates 34 studies from 2018 to 2025, finding that AI often exceeds 90% accuracy and can match or outperform expert clinicians in diagnosing common ocular diseases such as diabetic retinopathy, glaucoma, retinopathy of prematurity, and age-related macular degeneration, yet real-world deployment remains constrained by three gaps—disjointed integration into clinical workflows, lack of transparency in AI decision-making, and poor generalizability across diverse populations—and it proposes actionable pathways to bridge the "last-mile gap" between research and clinical practice.