An Overview of the Implementation of Artificial Intelligence in Clinical Pathways
Bibliographic record
Abstract
Abstract The increase in the use and development of artificial intelligence (AI) is a reality in the modern scientific community. In healthcare settings, the use of AI is spread in all areas and specialties, mostly given by the increase of big data and massive data sets available publicly and privately, together with the advance of computer technologies. Clinical pathways consist of a multidisciplinary, evidence-based approach to healthcare management that outlines the optimal sequence and timing of clinical interventions for the care of patients with a specific diagnosis or condition. The use of this evidence-based tool is related to data availability and impact on patient health outcomes. There are many contributions in the literature that analyze the impact and development of AI within clinical pathways. The objective of this study is to provide an overview of the literature on the application of AI to clinical pathways, analyzing the main characteristics, benefits, and limitations of part of the scientific contributions published in the last decade.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".