A Critical Review About the Application of Artificial Intelligence in Pain Assessment
Bibliographic record
Abstract
Pain is a serious health problem in both adults and infants. If left untreated, it results in serious physiological and psychological consequences. Therefore, accurate and quick pain assessment is crucial to avoid these consequences. While self-reports remain the gold standard in verbal humans, other pain assessment tools are used in infants and noncommunicative people, such as facial expressions, visual analog, and numerical rating scales. Manual pain assessment has several limitations, such as its subjective nature, inconsistency, and potential for bias originating from different sources, including observer gender and culture. Automated pain assessment has received much attention in the last few years, combining artificial intelligence (AI) with these manual tools to achieve accurate and objective pain assessment, especially in infants or nonverbal patients. However, a gap between developing AI models for pain assessment and their application exists. This gap needs to be addressed so that clinicians understand the limitations of using AI-powered pain assessment tools. This paper provides an overview of common pain assessment tools powered with AI, which are facial expressions, body and head movements, language analysis, electrodermal activity, and electroencephalography. In addition, it discusses the gap between the AI models developed based on these tools and their applications under clinical conditions.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".