Comparative Analysis of the Proposed AI-Based Monitoring Framework with Existing Pain Assessment Practices and Quantification of Improvements

Kanchan Chaudhary; Saurabh Charaya

doi:10.63363/aijfr.2025.v06i06.2084

Comparative Analysis of the Proposed AI-Based Monitoring Framework with Existing Pain Assessment Practices and Quantification of Improvements

Author(s)	Kanchan Chaudhary, Saurabh Charaya
Country	India
Abstract	Pediatric pain assessment remains a clinical challenge due to young patients’ limited communication abilities and the subjective nature of observational scales. This study compares a novel artificial intelligence (AI)-based multimodal pain monitoring framework against traditional pain assessment practices such as the FLACC (Face, Legs, Activity, Cry, Consolability) behavioral scale and the Neonatal Infant Pain Scale (NIPS). The AI framework integrates facial expression analysis, cry audio features, and physiological signals to continuously estimate pain levels. We present a comprehensive evaluation of accuracy, sensitivity, reliability, timeliness, and interpretability improvements. Results indicate that the AI system achieves high pain detection accuracy (~90%) and sensitivity (~91%), significantly outperforming single-modality or vital-signs–only assessments. The AI’s pain scores show strong correlation with expert FLACC ratings (r = 0.88, p < 0.001), confirming convergent validity with current standards. Critically, the automated system provides real-time monitoring, detecting pain episodes within seconds, whereas traditional nurse-led assessments are periodic and may miss transient pain peaks. The AI framework also demonstrated a 50% reduction in missed severe pain incidents and greatly reduced documentation time through automation. Clinicians in a pilot setting reported improved confidence in pain management due to the AI’s continuous vigilance and its visual explanations of pain cues. In conclusion, the proposed AI-based monitoring framework substantially enhances postoperative pediatric pain assessment in accuracy and responsiveness, while maintaining interpretability, thereby addressing key limitations of prevailing methods. These findings support the integration of AI-driven pain monitoring into clinical practice to augment patient comfort and outcomes.
Keywords	Pediatric pain assessment, FLACC scale, Neonatal Infant Pain Scale, Multimodal AI, Postoperative pain, Real-time monitoring, Interpretability
Field	Computer Applications
Published In	Volume 6, Issue 6, November-December 2025
Published On	2025-11-18
DOI	https://doi.org/10.63363/aijfr.2025.v06i06.2084
Short DOI	https://doi.org/hbbz7q

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Comparative Analysis of the Proposed AI-Based Monitoring Framework with Existing Pain Assessment Practices and Quantification of Improvements

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