Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions

Area of Science:

• Ophthalmology
• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Ocular surface tumors (OSTs) are rare, potentially life-threatening neoplasms.
• Accurate diagnosis relies on expert interpretation and advanced imaging, with excisional biopsy as the gold standard.
• Current diagnostic limitations highlight the need for less invasive, accessible methods.

Purpose of the Study:

• To provide a comprehensive review of artificial intelligence (AI) advancements in the management of ocular surface tumors (OSTs).
• To examine the current state and future potential of AI in diagnosing and managing OSTs.

Main Methods:

• Review of AI models applied to ophthalmic tumors using imaging data.
• Classification of AI diagnostic tools based on imaging modalities and OST types.
• Discussion of challenges and future directions for AI implementation in clinical ophthalmology.

Main Results:

• AI demonstrates significant promise in enhancing OST diagnosis and patient management.
• AI models are being developed using various imaging modalities for specific OSTs.
• Key challenges include data limitations and ethical considerations.

Conclusions:

• AI holds substantial potential to improve OST diagnosis and management, bridging technological innovation with clinical needs.
• Further research and development are crucial for integrating AI into routine clinical ophthalmology practice.
• AI can ultimately lead to improved patient outcomes for challenging OST conditions.