Clinical Scorecard: Cascading AI Into the Clinic
At a Glance
| Category | Detail |
|---|---|
| Condition | Macular Diseases |
| Key Mechanisms | Artificial intelligence tools for eye care and oculomics |
| Target Population | Patients with macular diseases, including age-related macular degeneration (AMD) |
| Care Setting | Routine clinical care in community optometry, high-street settings, and specialist hospital clinics |
Key Highlights
- Cascader aims to commercialize AI tools for eye care.
- Initial focus on macular diseases, particularly AMD.
- Collaboration with Moorfields Eye Hospital and UCL Ventures.
- AI developed can analyze optical coherence tomography scans.
- Support from the INSIGHT Eye and Oculomics Health Data Research Hub.
Guideline-Based Recommendations
Diagnosis
- Utilize AI for referral recommendations for sight-threatening conditions.
Management
- Implement AI tools to enhance early diagnosis and treatment.
Monitoring & Follow-up
- Leverage ocular imaging and data for insights into systemic conditions.
Risks
- Potential challenges in transitioning AI from research to clinical application.
Patient & Prescribing Data
Individuals with macular diseases and systemic conditions linked to ocular health.
AI tools aim to improve detection and management of diseases.
Clinical Best Practices
- Integrate AI tools into routine eye care practices.
- Utilize curated datasets for AI development in clinical settings.
- Focus on translating academic research into practical applications.
Related Resources & Content
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