Selection and Pilot Implementation of a Mobile Image Viewer: A Case Study
Bibliographic record
Abstract
BACKGROUND: For health care providers, mobile image viewing increases image accessibility, which could lead to faster interpretation/consultations and improved patient outcomes. OBJECTIVE: We explored the technical requirements and challenges associated with implementing a commercial mobile image viewer and conducted a small study testing the hypothesis that the mobile image viewer would provide faster image access. METHODS: A total of 19 clinicians (9 radiologists, 3 surgeons, 4 neurologists, and 3 physician assistants) evaluated (1) a desktop commercial picture archiving and communication system (PACS) viewer, (2) a desktop viewer developed internally over 20 years and deployed throughout the enterprise (ENTERPRISE viewer) and (3) a commercial Food and Drug Administration class II-cleared mobile viewer compatible with Web browsers, tablets, and mobile phones. Data were collected during two separate 7-day periods, before and after mobile image viewer deployment. Data included image viewer chosen, time to view first image, technical issues, diagnostic confidence, and ease of use. RESULTS: For 565 image-viewing events, ease of use was identical for PACS and mobile viewers (mean 3.6 for all scores of a possible 4.0), and significantly worse for the enterprise viewer (mean 2.9, P=.001). Technical issues were highest with the enterprise viewer (26%, 56/215) compared with the mobile (7%,19/259, P=.001) and PACS (8%, 7/91, P=.003) viewers. Mean time to first image for the mobile viewer (2.4 minutes) was significantly faster than PACS (12.5 minutes, P=.001) and the enterprise viewer (4.5 minutes, P=.001). Diagnostic confidence was similar for PACS and mobile viewers and worst for enterprise viewer. Mobile image viewing increased by sixfold, from 14% (37/269, before the deployment) to 88.9% (263/296, after the deployment). CONCLUSIONS: A mobile viewer provided faster time to first image, improved technical performance, ease of use, and diagnostic confidence, compared with desktop image viewers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".