<title>New trends in radiology workstation design</title>
Bibliographic record
Abstract
In the radiology workstation design, the race for adding more features is now morphing into an iterative user centric design with the focus on ergonomics and usability. The extent of the list of features for the radiology workstation used to be one of the most significant factors for a Picture Archiving and Communication System (PACS) vendor's ability to sell the radiology workstation. Not anymore is now very much the same between the major players in the PACS market. How these features work together distinguishes different radiology workstations. Integration (with the PACS/Radiology Information System (RIS) systems, with the 3D tool, Reporting Tool etc.), usability (user specific preferences, advanced display protocols, smart activation of tools etc.) and efficiency (what is the output a radiologist can generate with the workstation) are now core factors for selecting a workstation. This paper discusses these new trends in radiology workstation design. We demonstrate the importance of the interaction between the PACS vendor (software engineers) and the customer (radiologists) during the radiology workstation design. We focus on iterative aspects of the workstation development, such as the presentation of early prototypes to as many representative users as possible during the software development cycle and present the results of a survey of 8 radiologists on designing a radiology workstation.
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 | 0.000 | 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".