Guidelines International Network: Principles for Use of Artificial Intelligence in the Health Guideline Enterprise
Bibliographic record
Abstract
DESCRIPTION: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise. METHODS: A GIN working group on AI developed these principles, informed by the results of a scoping review and practical examples, through iterative discussion. RECOMMENDATIONS: Eight principles were identified to adhere to when using AI in the guideline context: transparency, preplanning, additionality, credibility, ethics, accountability, compliance, and evaluation. These complementary principles are described in a comprehensive way, but they do not provide detailed instructions on how to use specific AI tools. Although these principles are expected to apply across different contexts and stages of the guideline enterprise, details on their implementation have some degree of flexibility. Guideline development groups choosing to use AI will be able to adequately implement the principles if they ensure aspects such as structured reporting on the use of AI tools, involvement of experts in AI, and allocation of funding for the adequate use of AI tools. The GIN principles may support guideline developers in the responsible and transparent use of AI to ensure trustworthy guidelines.
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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 | 0.121 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".