Framework for Environmentally Sustainable Radiology: Call for Collaborative Action and a Health-Centered Focus
Bibliographic record
Abstract
It is imperative that the entire medical imaging sector acts collectively and decisively to reduce its own environmental impact and prepare for the current and future effects of the climate crisis. The Radiology R7 meeting convened in Venice, Italy, on October 10-13, 2024 to discuss environmental sustainability and other key issues facing radiology and the patients served by medical imaging. Radiology R7 delegates agree that collaborative action is urgently needed to transform radiology systems to be climate-resilient, equitable, low-carbon, and sustainable. This special report highlights priorities and outlines a framework for environmentally sustainable radiology, centered on eight collaborative action areas. A health-centered response reinforces the role of radiologists as physicians, emphasizes the opportunity for medical imaging to improve health, and will be essential to engage key partners in climate action. Effective leadership and governance are needed to ensure that radiology services are accessible, equitable, affordable, high quality and sustainable. Collaboration and partnership are essential to achieve meaningful change. Health equity should be prioritized to increase global access to high quality radiology services while minimizing the environmental impact. Multiple climate response pathways should be implemented in parallel including mitigation strategies to reduce the use of energy, finite resources and waste and adaptation strategies to build resilience to the effects of climate change. Innovation and research are necessary to develop, validate, and implement sustainable solutions. Finally, knowledge sharing, education, and training are needed to disseminate information on actions toward environmentally sustainable radiology practices. We all have a role to play and must work together to achieve these aims quickly by identifying the problem, setting goals, implementing a plan, measuring impact, sharing results, and celebrating successes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".