Transforming the future of ophthalmology: artificial intelligence and robotics' breakthrough role in surgical and medical retina advances: a mini review

Eleftherios Chatzimichail ¹, Nicolas Feltgen ¹, Lorenzo Motta ²

⁰Department of Ophthalmology, University Hospital of Basel, Basel, Switzerland.

Frontiers in Medicine +

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July 30, 2024

View abstract on PubMed

Summary

Artificial intelligence (AI) and robotic systems are transforming ophthalmology, especially in retinal imaging and surgery. While AI improves diagnostic accuracy, robotic surgery faces cost and training barriers, limiting its widespread adoption in retinal care.

Area Of Science

Ophthalmology
Medical Technology
Artificial Intelligence in Medicine

Background

Artificial intelligence (AI), deep learning, and machine learning are increasingly integrated into ophthalmology, particularly for ophthalmic imaging analysis.
Automated diagnostic algorithms for retinal conditions require extensive training on large retinal image datasets.
Robotic technology is advancing in medical fields, with a focus on anterior segment and vitreoretinal surgery in ophthalmology.

Purpose Of The Study

To review current research on the implementation of AI and robotic systems in ophthalmology, specifically for retinal conditions.
To discuss the challenges and limitations hindering the widespread adoption of these advanced technologies in clinical practice.

Main Methods

Review of current literature and research on artificial intelligence, machine learning, deep learning, and robotic surgery in ophthalmology.
Analysis of the impact of AI in automated diagnosis of retinal diseases and its role in telemedicine.
Examination of robotic surgical systems for anterior segment and vitreoretinal procedures, including their benefits and drawbacks.

Main Results

AI algorithms show promise in enhancing diagnostic accuracy for various retinal diseases, especially for non-specialist clinicians and in telemedicine settings.
Robotic surgical systems offer potential improvements in precision and tremor reduction for ophthalmic surgeries.
Significant barriers, including high costs and surgeon learning curves, currently limit the broad implementation of robotic surgical systems in ophthalmology.

Conclusions

AI is demonstrating a growing positive impact on the diagnosis of retinal conditions, improving accessibility and accuracy.
Robotic systems in ophthalmology, while promising, face substantial challenges that impede widespread clinical adoption.
The integration of AI and robotic systems in retinal care is limited but expanding, necessitating further research and development to overcome existing hurdles.

Keywords:

artificial intelligence macular degeneration medical retina retinal conditions retinopathy of prematurity robotics surgical retina vitreoretinal surgery