Environmental Sustainability and Cancer Imaging
Bibliographic record
Abstract
The rising global burden of cancer drives increased demands for medical imaging, which is essential throughout cancer care. However, delivering medical imaging presents significant environmental challenges including high energy use, reliance on single-use supplies, and the production of environmental pollutants. Environmental factors, such as ultraviolet radiation, wildfire smoke, and carcinogenic pollutants contribute to rising cancer rates, while extreme weather events driven by climate change disrupt cancer care delivery-highlighting the close connection between patient and planetary health. This review explores opportunities to improve the environmental sustainability of oncologic imaging, emphasizing the importance of patient-relevant outcomes-such as quality of life and overall survival-as a guiding principle in cancer care. Key strategies include optimizing imaging schedules to reduce low-value imaging, selecting modalities with lower environmental impact where clinically appropriate, minimizing waste streams, and adopting energy-efficient practices. Artificial intelligence offers the potential to personalize imaging schedules and improve efficiency, though its benefits must be weighed against energy use. Mobile imaging programs and integrated scheduling reduce patient travel-related emissions while promoting health equity, particularly in underserved communities. Future research should focus on optimizing imaging intervals to address patient-relevant outcomes better, expanding the use of abbreviated imaging protocols, and the judicious deployment of artificial intelligence, ensuring its benefits justify energy use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".