Using artificial intelligence for systematic review: the example of elicit
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) tools are increasingly being used to assist researchers with various research tasks, particularly in the systematic review process. Elicit is one such tool that can generate a summary of the question asked, setting it apart from other AI tools. The aim of this study is to determine whether AI-assisted research using Elicit adds value to the systematic review process compared to traditional screening methods. METHODS: We compare the results from an umbrella review conducted independently of AI with the results of the AI-based searching using the same criteria. Elicit contribution was assessed based on three criteria: repeatability, reliability and accuracy. For repeatability the search process was repeated three times on Elicit (trial 1, trial 2, trial 3). For accuracy, articles obtained with Elicit were reviewed using the same inclusion criteria as the umbrella review. Reliability was assessed by comparing the number of publications with those without AI-based searches. RESULTS: The repeatability test found 246,169 results and 172 results for the trials 1, 2, and 3 respectively. Concerning accuracy, 6 articles were included at the conclusion of the selection process. Regarding, revealed 3 common articles, 3 exclusively identified by Elicit and 17 exclusively identified by the AI-independent umbrella review search. CONCLUSION: Our findings suggest that AI research assistants, like Elicit, can serve as valuable complementary tools for researchers when designing or writing systematic reviews. However, AI tools have several limitations and should be used with caution. When using AI tools, certain principles must be followed to maintain methodological rigour and integrity. Improving the performance of AI tools such as Elicit and contributing to the development of guidelines for their use during the systematic review process will enhance their effectiveness.
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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 | 0.762 | 0.864 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.048 | 0.041 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".