<title>Access control and confidentiality in radiology</title>
Bibliographic record
Abstract
A medical record contains a large amount of data about the patient such as height, weight and blood pressure. It also contains sensitive information such as fertility, abortion, psychiatric data, sexually transmitted diseases and diagnostic results. Access to this information must be carefully controlled. Information technology has greatly improved patient care. The recent extensive deployment of digital medical images made diagnostic images promptly available to healthcare decision makers, regardless of their geographic location. Medical images are digitally archived, transferred on telecommunication networks, and visualized on computer screens. However, with the widespread use of computing and communication technologies in healthcare, the issue of data security has become increasingly important. Most of the work until now has focused on the security of data communication to ensure its integrity, authentication, confidentiality and user accountability. The mechanisms that have been proposed to achieve the security of data communication are not specific to healthcare. Data integrity can be achieved with data signature. Data authentication can be achieved with certificate exchange. Data confidentiality can be achieved with encryption. User accountability can be achieved with audits. Although these mechanisms are essential to ensure data security during its transfer on the network, access control is needed in order to ensure data confidentiality and privacy within the information system application. In this paper, we present and discuss an access control mechanism that takes into account the notion of a care process. Radiology information is categorized and a model to enforce data privacy is proposed.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".