Infrastructure for secure medical image sharing between distributed PACS and DI-r systems.
Bibliographic record
Abstract
Recent developments in information and communication technologies and their incor-\nporation into the medical domain have opened doors for the enhancement of health care\nservices and thereby increasing the work \now at a reasonable rate. However, to implement\nsuch services, current medical system needs to be \nexible enough to support integration\nwith other systems. This integration should be achieved in a secure manner and the\nresultant service should be made available to all health professionals and patients. This\nthesis proposes a new infrastructure for secure medical image sharing between legacy\nPACS and DI-r. The solution employs OpenID standard for user authentication, OAuth\nservice to grant authorization and IHE XDS-I pro les to store and retrieve medical im-\nages and associated meta data. In the proposed infrastructure cooperative agents are\nemployed to provide a user action, patient consent and system policy based access con-\ntrol mechanism to securely share medical images. This allows safe integration of PACS\nand DI-r systems within a standard EHR system. In addition to this, a behavior-pattern\nbased security policy enhancement feature is added to the system to assist the system\nsecurity administrator. The resulting secure and interoperable medical imaging systems\nare easy to expand and maintain. Behavior of the entire system is analysed using general-\npurpose model driven development tool IBM Rational Rhapsody. The code generation\nand animation capability of the tool makes it powerful for running e ective simulations.\nWe mainly explore the use of state charts and their interactions with MySQL database\nto learn the behavior of the system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".