Interaction techniques for radiology workstations: impact on users' productivity
Bibliographic record
Abstract
As radiologists progress from reading images presented on film to modern computer systems with images presented on high-resolution displays, many new problems arise. Although the digital medium has many advantages, the radiologist’s job becomes cluttered with many new tasks related to image manipulation. This paper presents our solution for supporting radiologists’ interpretation of digital images by automating image presentation during sequential interpretation steps. Our method supports scenario based interpretation, which group data temporally, according to the mental paradigm of the physician. We extended current hanging protocols with support for “stages”. A stage reflects the presentation of digital information required to complete a single step within a complex task. We demonstrated the benefits of staging in a user study with 20 lay subjects involved in a visual conjunctive search for targets, similar to a radiology task of identifying anatomical abnormalities. We designed a task and a set of stimuli which allowed us to simulate the interpretation workflow from a typical radiology scenario - reading a chest computed radiography exam when a prior study is also available. The simulation was possible by abstracting the radiologist’s task and the basic workstation navigation functionality. We introduced “Stages,” an interaction technique attuned to the radiologist’s interpretation task. Compared to the traditional user interface, Stages generated a 14% reduction in the average interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".