Legal and ethical considerations around the use of existing illustrations to generate new illustrations in the anatomical sciences
Bibliographic record
Abstract
It is likely existing anatomical illustrations are often used as the basis for new illustrative works, given not all illustrators have access to human tissues, bodies, or prosections on which to base their illustrations. Potential issues arise with this practice in the realms of copyright infringement and plagiarism when authors are seeking to publish, a matter becoming more prevalent with the proliferation in publishing platforms and the increased adoption of generative artificial intelligence applications within academia. However, there is little published guidance that might inform authors when using an existing illustration as the basis for new work. This article provides information pertaining to copyright, copyright infringement, fair use and fair dealings, plagiarism, and the overlap of copyright and plagiarism to highlight issues of law and ethics that are relevant to the creation of illustrations. Interestingly, the determination of exactly what constitutes an "original" illustration per construction from a secondary source has not been determined in case law for anatomy illustrations. This fact illuminates the absence of a "bright-line" test for illustration reproduction and the difficulties in the objective assessment of what constitutes a "nonoriginal" illustration. The term "substantively different" is useful for determining whether illustrations derived from secondary sources can be deemed original. This article delivers guidance on how to develop illustrations with reference to determining whether copyright has been breached or plagiarism has occurred. It also provides information that will direct decision-making around illustrative content.
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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 | 0.129 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".