Objective:

To explore the application of artificial intelligence in improving specific aspects of contact lens fitting practices, such as accuracy and efficiency.

Key Findings:

• AI models, particularly convolutional neural networks, outperform traditional fitting methods, as evidenced by the studies.
• Machine learning algorithms enhance first-fit success rates in contact lens fitting, demonstrated in the referenced studies.
• Large databases can reveal trends that lead to better clinical outcomes, supporting the effectiveness of AI methods.

Interpretation:

The integration of AI in contact lens fitting is poised to revolutionize the industry, leading to higher accuracy and efficiency in practices, as seen in the studies.

Limitations:

• The studies referenced are retrospective and may not account for all variables in clinical settings, potentially limiting their applicability.
• The transition to AI methods may require significant training and adaptation from practitioners to ensure effective implementation.

Conclusion:

AI is becoming mainstream in contact lens fitting, promising significant improvements in first-fit success rates, which could transform practitioner approaches.