Current state evaluation of challenges and opportunities in standardized nomenclature and artificial intelligence adoption in Canadian radiation oncology practice
Bibliographic record
Abstract
PURPOSE: Variations in the implementation of emerging data nomenclature standards and usage of artificial intelligence (AI) tools in Canadian radiation therapy (RT) centres have not yet been fully characterized. To address this, a current state analysis was conducted to serve as a baseline assessment and to identify gaps and opportunities for harmonized pan-Canadian data practices and the adoption of AI in clinical settings within radiation oncology. METHODS AND MATERIALS: A survey was distributed to all Canadian RT centres with the aim to describe the perceived status and characteristics of implementation of standardized nomenclature, usage of AI tools, and relevant gaps and opportunities in this field. RESULTS: Thirty three of 51 (64.7%) Canadian RT centres responded. Responses characterized variation in standardized nomenclature implementation and usage of AI tools across centres, with some trends between regions. Approximately two-thirds of RT centres were using TG-263 guidance of the American Association of Physicists in Medicine (70.0%, n=23). whereas only a third of centres reported awareness of next steps for O3. The most commonly-reported barriers to data standardization included a lack of resources and forcing functions. Automation and quality improvement were recognized as facilitators, with (81.8%, n=27) are using automation tools to support standardization. CONCLUSIONS: This current state analysis informs and directs future initiatives to improve standardized nomenclature implementation and support informed AI adoption within RT, with the goal of ultimately improving RT quality and safety. Canada is well-positioned to lead data standardization efforts and serve as a case study to potentially provide guidance at an international level to equal partners.
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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 | 0.054 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".