Objective:

To explore the environmental implications of integrating artificial intelligence (AI) in ophthalmology, particularly in relation to its computational demands and resource usage.

Approach:

• Overview of AI in Ophthalmology: Discusses the rapid transformation in ophthalmology due to AI, including automated screening and clinical decision support systems, and highlights the need for environmental considerations.
• Environmental Impact Considerations: Highlights the overlooked environmental consequences of AI technologies, such as energy consumption and water usage, compared to traditional waste sources in healthcare.
• Literature Review: Reviews existing literature on the environmental implications of AI in medicine, noting a significant gap in ophthalmology-specific studies addressing these impacts.
• Call for Sustainability Evaluation: Advocates for the integration of sustainability assessments alongside safety and effectiveness in evaluating AI technologies, as emphasized in recent literature.

Key Findings:

• AI systems require extensive computational resources, contributing to a significant environmental footprint, including energy consumption and water usage.
• The production of hardware for AI involves critical mineral extraction and substantial water usage, raising environmental concerns.
• Ophthalmology lacks literature addressing the environmental ramifications of AI despite its heavy reliance on digital imaging and data processing.
• The environmental impact of AI, including carbon footprint and energy consumption, is not adequately studied in ophthalmology, highlighting a critical gap.

Interpretation:

The integration of AI in ophthalmology is essential, but its environmental impact must be evaluated to ensure sustainable practices.

Limitations:

• Existing literature primarily focuses on conceptual discussions rather than empirical data on environmental impacts, particularly in ophthalmology.
• There is a lack of understanding regarding the carbon footprint and energy consumption associated with ophthalmic AI technologies.

Conclusion:

Sustainability should be a critical measure in the evaluation of AI technologies in healthcare, including ophthalmology.

Sources:

• Richie et al. Review
• Selvan et al. Research