ReviewCureus2025
Artificial Intelligence (AI)-Driven Approaches to Manage Postoperative Pain, Anxiety, and Psychological Outcomes in Surgical Patients: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.BMC anesthesiology · 2026Article
- Use of Artificial Intelligence in Preoperative Planning in Surgery: A Narrative Review.Cureus · 2026Review
- Patient-Centered Cardiac Surgery: Psychosocial Challenges, Evidence-Based Interventions, and Future Horizons.Healthcare (Basel, Switzerland) · 2025Review
- The Development and Evaluation of a Retrieval-Augmented Generation Large Language Model Virtual Assistant for Postoperative Instructions.Bioengineering (Basel, Switzerland) · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postoperative pain, anxiety, and psychological distress significantly impact surgical recovery, yet conventional management strategies often lack personalization. Artificial intelligence (AI) has emerged as a transformative tool in perioperative care, offering potential solutions through predictive analytics, real-time monitoring, and tailored interventions. This systematic review synthesizes evidence on AI-driven approaches for improving postoperative pain, anxiety, and psychological outcomes in surgical patients. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search of PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, Scopus, Web of Science, and Cochrane Library up to April 2025. Ten studies met the inclusion criteria. Risk of bias was assessed using the revised Cochrane risk of bias tool for randomized trials (ROB 2) and Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) for non-randomized studies. Data were narratively synthesized by AI applications (e.g., nociception monitoring, robotics, machine learning (ML)) and outcomes (pain, anxiety, psychological metrics). AI interventions demonstrated efficacy in reducing postoperative pain (e.g., nociception level (NOL)-guided analgesia lowered pain scores by 33% vs. standard care) and anxiety (e.g., interactive robots reduced pediatric preoperative anxiety). ML models predicted pain severity (area under the curve (AUC) up to 0.75) and complications (AUC 0.84) but showed lower accuracy for readmissions (AUC 0.66). Automated psychological interventions reduced opioid use by 36.5%. Limitations included small sample sizes (12 to 201 participants), heterogeneity in AI methods, and short follow-up durations. AI shows promise in personalizing perioperative care, particularly for pain and anxiety management, though standardization and larger trials are needed. Future research should prioritize robust validation, long-term outcomes, and integration into clinical workflows to translate AI's potential into routine practice.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.