Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke Care: A Systematic Review
Bibliographic record
Abstract
Abstract Established randomized-trial-based parameters for acute ischemic stroke care fail to consider individualized patient data, leading to attempts to support or automate treatment and diagnosis decisions using artificial intelligence methods. We review existing research, specifically regarding methodological robustness, thereby identifying constraints for clinical AI implementation. Our systematic review of clinical decision support systems (CDSS) includes full-text English language publications proposing AI-based methods for decision support in acute ischemic stroke cases in adult patients. We (a) describe data and outcomes used in those systems, (b) estimate the systems’ benefits compared to traditional stroke diagnosis and treatment, and (c) report concordance with the MINIMAR checklist. 121 studies met our inclusion criteria. 65 were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous; adherence to the MINIMAR checklist was low. Our results suggest significant validity threats, dissonance in reporting practices and challenges to clinical translation. We outline practical recommendations for successful implementation of AI in acute ischemic stroke treatment and diagnosis.Ela M. Akay and Adam Hilbert contributed equally
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 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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".