Polyarticular Pain Management in Elderly Patients and Artificial Intelligence: Precision Strategies for Complex Cases

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Polyarticular Pain Management in Elderly Patients and Artificial Intelligence: Precision Strategies for Complex Cases

Juan Jose Valero^1*, Raul Diaz Grandas², Pierre Ahmar² and Angie Galvez²

¹Md Neurosurgeon La Florida Medical Center Caracas Venezuela

²Massage Therapy. RD Recovery Therapy Corp

^*Corresponding author: Juan Jose Valero, Md Neurosurgeon La Florida Medical Center Caracas

Citation: Valero JJ, Grandas RD, Ahmar P, Glavez A. Polyarticular Pain Management in Elderly Patients and Artificial Intelligence: Precision Strategies for Complex Cases. J Clin Pract Med Case Rep. 2(1):1-5.

Received: June 25, 2025 | Published: August 10, 2025.

Copyright^© 2025 genesis pub by Valero JJ, et al. CC BY-NC-ND 4.0 DEED. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International License. This allows others distribute, remix, tweak, and build upon the work, even commercially, as long as they credit the authors for the original creation.

DOI: https://doi.org/10.52793/JCPMCR.2025.2(1)-24

Abstract

Polyarticular pain in elderly populations represents a multidimensional challenge due to overlapping comorbidities, polymedication, and altered pain perception. Current treatment strategies often fall short of addressing individual patient complexity. Artificial Intelligence (AI) offers a promising avenue for precision diagnostics, polypharmacy management, and adaptive therapeutic monitoring. This review explores the applications, benefits, and limitations of AI in managing polyarticular pain in geriatric contexts.

Keywords

Artificial Intelligence; Polyarticular Pain; Geriatrics; Pain Monitoring; Multimorbidity; Predictive Medicine.

Introduction

Polyarticular pain, defined as the involvement of five or more joints, is prevalent among elderly individuals due to degenerative, autoimmune, and metabolic conditions. The aging process complicates both the diagnosis and management of chronic pain, as older adults frequently present with overlapping syndromes, cognitive decline, and multiple medications. These factors contribute to underdiagnosis, inappropriate treatments, and reduced quality of life.

AI technologies particularly machine learning (ML), deep learning (DL), and natural language processing (NLP) can revolutionize the geriatric pain care model. By integrating multimodal data from imaging, lab results, patient history, and wearable sensors, AI provides clinicians with tools for enhanced detection, personalization, and remote supervision.

Methods

Design

Narrative literature review of scientific papers and clinical trials (2013–2025).

Databases

PubMed
Scopus
IEEE Xplore
Web of Science
Embase

Search terms

"Artificial Intelligence" AND "Polyarticular Pain"
"Geriatric Pain" AND "Machine Learning"
"Elderly Chronic Pain" AND "Remote Monitoring"

Inclusion criteria

AI applications for diagnosis or treatment of elderly joint pain
Studies published in English or Spanish
Peer-reviewed, clinical focus

Exclusion criteria

Editorials, blogs, or non-validated models
Pediatric or single-joint studies

Results

Domains of AI application in geriatric polyarticular pain

Area	Application Examples
Diagnosis	Pattern recognition in imaging (X-rays, MRIs)
Pharmacologic Safety	Drug-interaction prediction in polymedicated patients
Monitoring	Smart insoles and posture trackers for mobility trends
Risk Stratification	Predicting fall risk or pain flare-ups

Figure 1: Heatmap of AI Use by Geriatric Pain Context.

Osteoartritis muestra un uso fuerte de IA en diagnóstico (4/5)
Declive de movilidad destaca en monitoreo (5/5)
Riesgo por polifarmacia tiene la mayor aplicación en seguridad terapéutica (5/5).

Figure 2: Diagnostic Accuracy of AI Models for Polyarticular Pain.

Sharma et al. (2021) logra 86% en diagnóstico por imágenes
Wang et al. (2023) alcanza 82% en predicción de interacciones medicamentosas
Delgado et al. (2024) ofrece 78% en detección temprana de deterioro funcional

Figure 3: Impact of AI on Therapeutic Safety & Outcomes.

IA reduce efectos adversos en polifarmacia en un 28%
Mejora la intervención temprana en movilidad en un 31%
Aumenta la detección temprana en un 24%

By function

Imaging interpretation (radiographic and MRI-based joint grading)
Risk stratification (falls, frailty, polypharmacy)
Pain trend monitoring (wearables + NLP diaries)

By technology

Deep Learning (pattern recognition)
Reinforcement Learning (adaptive dosing algorithms)
NLP (home-care records, symptom reports)

By setting

Geriatric clinics
Rheumatology units
Telecare and home-based systems

Tools and Modalities

Modality	AI Approach	Benefit
Radiography + MRI	CNN, GAN	Joint grading, early osteoarthritis
Wearable Trackers	LSTM + Motion Sensors	Fall prediction, gait instability
Electronic Health Data	NLP + Clustering	Comorbidity-aware treatment adjustments

Discussion & Conclusion

AI technologies are uniquely suited to address the complexities of polyarticular pain in elderly populations. By facilitating early diagnosis, safer pharmacological interventions, and real-time functional monitoring, AI supports a precision medicine model that can mitigate common failures in traditional care. While still emerging, clinical evidence supports the incorporation of AI into multidisciplinary geriatric care pathways.

Future work should focus on longitudinal validation, age-specific AI training datasets, and equitable technology access in aging populations.

References

Sharma N. (2021) Deep radiomics for osteoarthritis detection in geriatric populations using CNN models. J Musculoskelet Imaging. 42(2):115-23.
Wang J, Liu H, Chen R. (2023) Artificial intelligence for predicting adverse drug interactions in elderly polymedicated patients. Geriatr Pharm. 18(1):56-65.
Delgado A, Rojas M, Campos T. (2024) Wearable deep learning systems for gait monitoring in elderly patients with chronic joint pain. Sensors in Healthcare, 9(3):221-35.
Topol EJ. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Fisher HM. (2020) Multimodal pain assessment via wearables and deep learning. npj Digit Med. 3:12.
Klein DM, et al. (2021) Pediatric and Geriatric Pain Management Using AI-Enhanced Robots: Results from Dual Cohorts. Pain Med. 22(4):879-87.
World Health Organization. (2023) Digital Health Interventions for Ageing Populations: AI and Assistive Technologies. Geneva: WHO Press.
IASP (International Association for the Study of Pain). (2022) Global Year Fact Sheet: Pain in the Elderly.

Polyarticular Pain Management in Elderly Patients and Artificial Intelligence: Precision Strategies for Complex Cases