Patient photographs on Google Images: a commentary on informed consent, copyright, and privacy laws
Bibliographic record
Abstract
Patient photographs published in medical journals have been found on Google Images in two studies, raising concerns about informed consent and patient privacy. It is unlikely that patient informed consent includes consideration of these circumstances, as there are no uniform guidelines for obtaining consent for the publishing of clinical photographs across online medical journals. Health data privacy legislation in the EU, US, and Canada is only concerned with identifiable patient photographs, and their guidelines for deidentification are inconsistent. Patients have limited legal recourse in such cases, as the liability of search engines, publishers, and clinicians is unclear under law. Better informed consent and publication practices, as well as ethical dissemination of sensitive information online by tech companies, are necessary to address these issues.
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 | 0.077 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.155 | 0.151 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".