Objective:
To explore how artificial intelligence (AI) is transforming the treatment planning for wet age-related macular degeneration (AMD).
Key Findings:
- AI can predict which patients may tolerate longer injection intervals and which may relapse early.
- The deepeye® TPS tool formalizes clinicians' mental forecasting of disease activity and treatment needs.
- AI-derived insights can shift treatment adherence from a behavioral issue to a planning challenge.
Interpretation:
AI-driven tools like deepeye® TPS can enhance treatment planning for wet AMD, aligning patient expectations with clinical realities and improving outcomes.
Limitations:
- Algorithmic recommendations must be transparent and challengeable.
- Validation of AI tools needs to extend beyond single-center datasets to ensure broad applicability.
Conclusion:
AI has the potential to bridge the gap between optimal drug efficacy and real-world patient experiences in wet AMD treatment.
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
