Co-Annotator: Expert-Distilled ViT and VLM for Visual and Documentation Guidance in Age-Related Macular Degeneration
cs.AI, cs.HC
Submitted: 2026-08-31
Updated: 2026-08-31
Comments: 23 pages, 11 figures. To appear in UIST '26: Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology, November 02-05, 2026, Detroit, MI, USA. DOI: 10.1145/3830398.3830722
License: http://creativecommons.org/licenses/by/4.0/
The gist: Clinical AI often optimizes predictive performance without engaging how clinicians decide where to look and what to write.
Terminology
Abstract
Clinical AI often optimizes predictive performance without engaging how clinicians decide where to look and what to write. We present Co-Annotator, which distills expert gaze and dictation into two guidance components: a gaze-aligned Vision Transformer producing fixation-aligned areas of interest (AOIs), and an ontology-bounded vision-language model (VLM) that pre-fills editable biomarker summaries for retinal optical coherence tomography (OCT). We first collect expert gaze and dictations (US1) to train the models, significantly improving diagnostic accuracy and biomarker generation. We then deploy the system with ophthalmology residents: a controlled resident study (US2) confirmed each modality is safe and independently beneficial, with AOI guidance producing lasting perceptual efficiency gains through post-guidance carryover and VLM guidance more than doubling biomarker documentation breadth. In a combined deployment across two academic institutions (US3), providing both modalities simultaneously produced efficiency gains that substantially exceeded either modality alone: correct diagnoses per minute increased by 40% and comment editing time fell by 67%, without compromising diagnostic accuracy. Notably, neither modality improved efficiency during guidance in US2, which makes the in-guidance efficiency gain under combined guidance in US3 the more striking result. Expert-distilled multimodal guidance can remove two distinct clinical workflow bottlenecks at once (visual search overhead and documentation burden) without compromising the diagnostic accuracy clinicians already achieve.
Sources
- RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
- MedGemma Technical Report
- Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
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