P.074 Assessing the emergence and evolution of artificial intelligence and machine learning research in neuroradiology
Bibliographic record
Abstract
Background: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and the field’s trends, challenges, and future directions. Methods: The American Journal of Neuroradiology was queried for original research articles published since inception (Jan. 1, 1980) to Sept. 19, 2022 that contained any of the following key terms: “machine learning”, “artificial intelligence”, or “radiomics”. Articles were screened, categorized into Statistical Modelling (Type 1), AI/ML Development (Type 2), or End-user Application (Type 3) and then bibliometrically analyzed. Results: A total of 124 articles were identified with 85% being non-integration focused (Type 1 n = 41, Type 2 n = 65) and the remaining (n = 18) being Type 3. The total number of articles published grew two-fold in the last five years, with Type 2 articles mainly driving this growth. While most (66%) Type 2 articles were led by a radiologist with 55% possessing a postgraduate degree, a minority of Type 2 articles addressed bias (15%) and explainability (20%). Conclusions: The results of this study highlight areas for improvement but also strengths that stakeholders can consider when promoting the shift towards integrating practical AI/ML solutions in neuroradiology.
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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 | 0.018 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".