Enhancing Environmental Sustainability in Diagnostic Radiology: Focus on CT, MRI, and Nuclear Medicine
Bibliographic record
Abstract
Medical imaging, including MRI, CT, and nuclear medicine play a critical role in healthcare but also imposes significant environmental burdens due to high energy consumption and waste production. Of the diagnostic modalities, MRI is the most energy-intensive modality, consuming up to 60 kWh per scan, followed by CT, which ranges from 1.0 to 11.4 kWh per scan. Lifecycle analyses show that operational energy use far exceeds manufacturing emissions, highlighting the need for energy-saving strategies. Implementing standby and power-off modes, optimizing scan protocols, and using AI-driven efficiency improvements can significantly reduce unnecessary energy use. Additionally, sustainable infrastructure, such as variable-flow cooling systems and strategic equipment placement, can further minimize environmental impact. Nuclear medicine, while relatively lower in energy consumption, relies on energy-intensive radioisotope production, often requiring fossil fuel-powered reactors and extensive transport logistics. Contrast agents in MRI and CT pose contamination risks in wastewater, as they are inadequately removed via conventional treatment plants methods. This results in the accumulation of gadolinium and iodinated byproducts in drinking water sources, posing potential human and ecological risks. Nuclear medicine radioisotopes, including Tc-99, also contribute to long-term contamination concerns. Strategies to mitigate these impacts include urine recycling, contrast separation, and advanced wastewater treatment. Sustainable practices in medical imaging require a multi-pronged approach, combining operational efficiency, renewable energy adoption, and stricter waste management protocols. Future efforts may also focus on promoting low-field MRI, AI-driven scan optimization, and alternative contrast agents, ensuring that radiology departments balance diagnostic efficacy with environmental responsibility.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".