The right to privacy of patient medical images in Iranian (Islamic) and Western law
Bibliographic record
Abstract
The use of images taken by the physician during the treatment and care of the patient can endanger the patient's privacy and image rights and lead to the disclosure of the patient's identity. The right to confidentiality of the patient's image therapy in the legal sphere is included in the category of the right to personality and in the realm of Islamic law is in the category of the sin against mankind. On the other hand, these images provide a unique opportunity for physicians in education and research. Given that based on the rights of persons to their image, no one can take a photo or video without the consent of a person, today, some domestic legal systems have provided the conditions for proper use of patient images to protect patients' rights and prevent possible disputes between physician and patient. Non-disclosure of patient identity, informed consent, and safe maintenance are the three main conditions in using patient images. Due to the necessity of finding the right to privacy of patient medical images, the present study, through a descriptive-analytic method, aims to achieve the principles and criteria of protection of this right for legislation in Iran by reviewing and analyzing the results of scientific theories and legal approaches of international centers such as the International Committee of Medical Journal Editors and the Canadian Medical Protective Association has considered and western countries such as Australia, England, and Canada.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".