An infrastructure for secure sharing of medical images between PACS and EHR systems
Bibliographic record
Abstract
New advances in information and communication technologies (ICT) and their incorporation into the medical domain have created opportunities to enhance medical services and provide improvement to workflow at a low cost. However, to implement such services, the current medical system needs to be integrated, secured, and available to health professionals and patients. In this paper, we propose an infrastructure that suggests the use of techniques and standards such as: cooperative multi-agents, standards for user authentication and service authorization, as well as protocols for cross-enterprise document sharing. The proposed infrastructure allows for integration of a PACS (Picture Archiving and Communication system) with a widely accepted HL7 (Health Level Seven) standard infrastructure for provisioning nation-wide electronic health records (EHR). In this approach, the cooperative agents provide: i) an action-based access control mechanism to share medical images that allow safe integration of a PACS and the Diagnostic Image Repository (DI-r) systems within a standard EHR system; and ii) a behavior-pattern based security polity enhancement to assist the system administrator. Such secure and interoperable medical imaging systems are easy to expand and maintain.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".