Global research trends on artificial intelligence in psychological interventions for stroke survivors: a bibliometric and visualized analysis (2000–2024)
Bibliographic record
Abstract
Objective: This study aimed to conduct a bibliometric analysis of research literature on AI-assisted psychological interventions for stroke survivors published from 2000 to 2024, using CiteSpace and VOSviewer to examine research collaboration networks, knowledge structures, and developmental trends. Methods: Literature data was sourced from the Web of Science Core Collection database (WoSCC). A total of 450 relevant articles, published between 1 January 2000 and 31 October 2024, were included. CiteSpace and VOSviewer were employed to analyze the literature from dimensions including authorship, country of origin, institutions, journals, references, and keywords. Results: Annual publication output has steadily increased, reaching a peak in 2022 (72 articles), followed by 68 articles in 2023 and 69 articles in 2024. Calabro Rocco Salvatore emerged as the most productive author (9 publications). The United States led in research output (87 articles) and centrality (0.51), with McGill University being the leading institution (10 articles). "Journal of NeuroEngineering and Rehabilitation" was the most productive journal (24 articles), while "Stroke" was the most co-cited journal (347 times). Recent research trends focused on ischemic stroke (strength = 3.09), anxiety (strength = 2.72), cognitive impairment (strength = 2.67), and meta-analysis (strength = 2.58), reflecting a shift toward integrated assessment and intervention strategies. Conclusion: This bibliometric analysis reveals a significant evolution in research on AI-assisted psychological interventions for stroke survivors, shifting from single technology applications to integrated services encompassing assessment, monitoring and intervention. Future research should continue to strengthen empirical studies while enhancing interdisciplinary collaboration to improve the quality of psychological healthcare services.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.144 | 0.140 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".