Clinical AI
AI Clinical Review in Ophthalmology: Before You Sign
Evaluate AI clinical review in ophthalmology: laterality, diagnosis and procedure context, source evidence, unresolved findings and clinician-led signing.
In this article
AI clinical review helps an ophthalmologist notice potential discrepancies in a consultation before signing. Its value is in presenting a specific issue alongside the relevant record, so the clinician can check the details and decide whether a correction is needed.
Clinical review in an EMR is the examination of a draft and its supporting information before finalization. AI can assist that review, while configured application checks and clinician sign-off keep their own roles.
Key takeaways:
- A useful finding identifies the affected field, eye and supporting context.
- Distinguish a potential mismatch from missing information or an unevaluated item.
- Check corrections against the underlying note and source record.
- Evaluate review quality separately from transcription and drafting quality.
How clinical review differs from a scribe
| Capability | Main question it helps answer |
|---|---|
| Ambient Scribe | What was said, and how should it appear in the draft? |
| Doctor Brief | What saved history and outstanding plans matter before the visit? |
| Clinical Review | Is there something in this consultation that needs another look? |
| Record-based AI answers | Which saved records answer this specific question? |
A well-written draft can still contain a wrong-eye entry, an inconsistent procedure description or an unsupported statement about completed care. Conversely, a review flag may have a valid explanation. The interface should make it easy to inspect that explanation without turning every finding into an alarm.
Start with eye-specific context
Consider a draft that describes a left-eye finding and a proposed procedure marked for the right eye. A useful review points to both entries and identifies the affected eye. The clinician can then determine whether one entry is incorrect or whether the record needs additional context.
This is a documentation scenario, not a rule that every diagnosis and procedure must match in a simple way. Test the hospital's actual use cases, including bilateral disease, prior surgery and subspecialty referrals. The review should preserve uncertainty when the available information does not support a conclusion.
Show the evidence and review status
| Result | What the user should understand |
|---|---|
| Potential mismatch | Which entries conflict and why they need inspection |
| Missing context | What information is absent from the available record |
| Not evaluated | That the item was outside the configured review coverage |
| Reviewed | Who considered the finding and what action they recorded |
An absence of findings is not proof that a consultation is error-free. Ask which checks ran, which inputs they used and how incomplete results are displayed. A green panel without that context tells the clinician very little.
Keep the correction and signing steps clear
The clinician should be able to open the relevant field, amend the draft and review the updated result. Signing remains a deliberate action by the responsible clinician. A review suggestion should not silently rewrite a signed record, create an investigation booking or change a bill.
The WHO's 2024 guidance on generative AI for health highlights automation bias and the possibility of incorrect output. A practical response is to show a concise reason and inspectable evidence while keeping the clinician's decision visible.
Evaluate useful findings and unnecessary interruptions
Run a controlled set of de-identified notes with known discrepancies, valid complex cases and deliberately missing information. Have the clinical team record which issues were identified, which were missed and which alerts did not require a change. Also test an unavailable or incomplete AI response.
Measure how easily a clinician can understand and resolve a finding. More alerts are not automatically better. Agree the scope with the team before interpreting any pilot result, and avoid quoting a sensitivity or accuracy percentage without a defined evaluation and reviewed evidence.
See the Iris clinical intelligence page for the product walkthrough, the ambient scribe checklist for documentation testing, and the pricing guide for questions about optional AI usage.
Frequently asked questions
Does clinical review make a diagnosis?
The workflow described here highlights information for clinician review. Examination, interpretation, diagnosis and treatment decisions remain with the qualified clinician.
Does no warning mean the note has passed every possible check?
No. The result depends on the available record and configured review coverage. The interface should distinguish checked items, missing information and items that were not evaluated.
Is clinical review the same as checking a transcript?
No. Transcript review checks what was captured from speech. Clinical review considers the draft and its relevant record context. Both need to be evaluated in their own right.