Quick Summary
AI radiography is moving beyond triage to risk prediction. It can flag subtle findings, prioritize high risk scans, and forecast disease progression. To scale safely, it needs diverse data, clear rules, and interoperable systems. Aether provides the connected record that ties imaging, labs, and outcomes together.
Why this matters
Radiology capacity is uneven. In many cities, one radiologist may read hundreds of images a day. AI can act as a second reader and triage assistant, helping reduce misses and delays.
From detection to prediction
When imaging is linked with patient history and labs, models can estimate risk of progression or relapse. This changes radiology from a static snapshot to a dynamic view over time.
Challenges in adoption
- Data diversity across regions and devices.
- Evolving standards for clinical validation and oversight.
- Integration barriers with legacy PACS and EHRs.
How Aether fits
- Standardize imaging results alongside labs and prescriptions.
- Track how AI risk scores change on a patient timeline.
- Share context securely between clinics and specialists.
The Aether Health Graph turns individual scans into connected information that supports responsible AI use across settings.
Sources and further reading
Information only. Not medical advice.
Next steps
- Upload recent X-ray and CT reports to your Aether account.
- Add notes on symptoms and treatments to see trends across time.
- Share a read only view with your clinician or specialist.