Diff View

The interface renders the original AI output and the corrected version side-by-side or stacked vertically, using color-coded inline spans to mark specific character-level changes.

• Green highlights indicate tokens added by the reviewer
• Red strikethroughs mark tokens removed by the reviewer
• Yellow underlines flag tokens with modified attributes or coding

This granularity allows a clinical reviewer to instantly verify that only the intended Protected Health Information was redacted or that a SNOMED CT code was correctly swapped without re-reading the entire document.

For hierarchical clinical data like FHIR bundles or medication administration records, the diff view collapses complex JSON or XML structures into a navigable tree format.

• Added nodes are marked with a plus icon and a distinct background
• Deleted nodes are grayed out with a minus icon
• Modified key-value pairs display both the old and new values in a split cell

This prevents reviewers from needing to parse raw code, allowing them to focus on semantic changes such as a corrected dosage quantity or an updated observation status from 'preliminary' to 'final'.

Each discrepancy in the diff view is visually anchored to the model's calibrated probability score, enabling rapid risk-based triage.

• Low-confidence spans (< 0.7) are rendered with a high-contrast, pulsating border to immediately draw the reviewer's eye
• High-confidence spans (> 0.95) are displayed with a subtle, muted indicator
• Hovering over any span reveals the exact confidence score and the top alternative predictions

This directs the reviewer's cognitive resources to the most likely error sites first, directly combating alert fatigue and optimizing the review cadence.

A persistent, synchronized panel links every AI-generated assertion and its subsequent human correction to the exact source sentence in the original clinical document.

• Clicking on a diff highlight scrolls the source viewer to the originating text
• The originating phrase is highlighted with a matching color for instant cross-referencing
• This creates a chain of custody for every data point, from source text to AI extraction to human validation

This feature is critical for discrepancy resolution and maintaining a compliant audit trail, allowing reviewers to verify clinical evidence without switching contexts.

When a reviewer corrects a recurring AI error within the diff view, the interface offers to propagate that identical correction across the entire batch or patient record.

• A notification appears: 'Apply this correction to 12 other instances?'
• The system uses semantic similarity matching to identify identical error patterns
• A preview panel shows all affected instances before the reviewer confirms the bulk action

This mechanism dramatically reduces the review burden and ensures correction propagation maintains data consistency across a longitudinal record, preventing contradictory entries.

Each correction made in the diff view can be tagged with a predefined error taxonomy label, turning the review process into a structured data collection exercise.

• A dropdown menu offers categories like 'Boundary Error', 'Negation Miss', 'Wrong Code', or 'Hallucination'
• Tagging is optional but encouraged, with a keyboard shortcut for speed
• Aggregated tags feed into a dashboard for concept drift detection and targeted active learning

This transforms the review interface from a simple correction tool into a strategic asset for evaluation-driven development and model retraining prioritization.