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AI in Perioperative Pain: A Scoping Review Protocol to Map the Evidence Landscape
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Lead
This protocol outlines a scoping review to characterize AI applications in perioperative pain management. It aims to assess methodological rigor, validation, and clinical implementation readiness across diverse AI methodologies and data modalities, addressing the current fragmentation of evidence in this critical care domain.
Key Facts
| Feature | Detail |
|---|---|
| Study Type | Scoping Review Protocol |
| Methodology | Joanna Briggs Institute (JBI) methodology; PRISMA-ScR reporting |
| Primary Objective | Characterize AI evidence for perioperative pain outcomes and identify methodological features affecting clinical credibility |
| Timeline | Searches planned for Sept 2026; completion expected Dec 2026 PMID: 42790900 |
Why It Matters / Context
Perioperative pain is a significant determinant of patient experience and can influence long-term outcomes, including the development of chronic postsurgical pain. While artificial intelligence is increasingly applied to perioperative datasets to predict acute postoperative pain, opioid requirements, analgesic-related adverse events, and pain trajectories, the current evidence base is dispersed. It spans various perioperative phases, clinical outcomes, data modalities, and AI methodologies.
A critical gap remains regarding the extent to which current models address validation, interpretability, uncertainty, and clinical implementation. As AI tools move from theoretical models to clinical practice, understanding their methodological strengths and weaknesses is essential for ensuring patient safety and effective care delivery. This protocol seeks to provide a comprehensive map of the existing evidence, highlighting which methodological features most strongly affect the clinical credibility and implementability of these AI systems. By following established JBI and PRISMA-ScR guidelines, the review aims to provide a rigorous, transparent, and reproducible assessment of the field.
Details
The protocol defines a comprehensive scope for identifying relevant studies. The following table summarizes the key components of the planned review:
| Component | Specification |
|---|---|
| Eligible Studies | Human research evaluating AI methods for pain-related perioperative outcomes, including observational, interventional, and predictive modelling studies PMID: 42790900 |
| AI Approaches | Machine learning, deep learning, natural language processing, computer vision, large language models, and hybrid/ensemble methods PMID: 42790900 |
| Clinical Outcomes | Acute postoperative pain, opioid requirements, analgesic-related adverse events, chronic postsurgical pain, and pain trajectories PMID: 42790900 |
| Data Sources | PubMed/MEDLINE, Embase, Cochrane Library, medRxiv, arXiv, ClinicalTrials.gov, and supplementary reference list screening PMID: 42790900 |
| Synthesis Method | Descriptive synthesis of results PMID: 42790900 |
| Ethical Charting | Governance of retrospective EHR use, consent waivers, and privacy protections PMID: 42790900 |
| Registration | Prospectively registered on Open Science Framework in March 2026 PMID: 42790900 |
The review will specifically chart ethical aspects reported in included studies, such as the governance of retrospective electronic health record use, consent waivers, and privacy protections, where available. This focus on ethics is crucial given the sensitive nature of pain data and the potential for bias in AI models trained on historical clinical data. The findings will be disseminated through submission to a peer-reviewed journal, presentation at scientific meetings, and open sharing of search strategies on the Open Science Framework, promoting transparency and reproducibility in the research process.
Limitations
Important Note: This is a protocol for a scoping review, not the final review itself. Therefore, it does not present primary data, statistical results, or definitive conclusions about the efficacy of AI in perioperative pain management. The actual findings, including the number of included studies, specific methodological gaps identified, and synthesized evidence, will only be available upon completion of the review, expected in December 2026 PMID: 42790900. Readers should not interpret this protocol as evidence of clinical benefit or harm.
Clinical Takeaway
Clinicians and researchers should monitor the final results of this scoping review to understand the current state of validation and interpretability in AI-driven perioperative pain models before integrating such tools into clinical workflows.
Full Reference
Alfieri A, Cerrone V, Di Franco S, Cascella M, De Feo R, Piazza O. Artificial intelligence in perioperative pain: a scoping review protocol. BMJ Open. 2026. PMID: 42790900 [DOI: 10.1136/bmjopen-2026-124432]