How to Use AI for Pain Tracking: A Guide for Personalized Digital Health

The experience of pain is fundamentally subjective, yet its effective management relies on objective, consistent tracking. For decades, the gold standard for pain assessment has been the patient’s self-report, typically via a simple 0-10 numerical rating scale. However, this method is prone to recall bias and inconsistency. The emergence of Artificial Intelligence (AI) in digital health is fundamentally transforming this landscape, offering new, objective, and highly personalized methods for tracking and understanding pain.

This article explores the practical ways individuals and healthcare professionals can leverage AI for pain tracking, moving beyond traditional methods to embrace a more data-driven approach to pain management.

The AI-Driven Shift from Subjective to Objective Tracking

AI’s role in pain tracking is to translate the subjective experience of pain into quantifiable data points. This is achieved by analyzing multimodal data—information gathered from various sources—to create a comprehensive pain profile [1].

1. Analysis of Non-Verbal Cues

One of the most significant advancements is the use of AI to analyze non-verbal expressions of pain, particularly in patients who are non-communicative (e.g., infants, patients with dementia, or those under heavy sedation).

• Facial Expression Analysis: AI models, often utilizing deep learning, are trained on vast datasets of video footage to recognize micro-expressions associated with pain. Tools like PainChek use a smartphone camera to capture a short video of a person's face, then apply AI algorithms to analyze the facial action units (e.g., brow furrowing, eye closing) and assign an objective pain score [2].
• Body Movement and Posture: Wearable sensors and video analysis can track changes in gait, posture, and movement patterns. For instance, a person experiencing chronic low back pain may exhibit subtle, consistent changes in their walking pattern that an AI can detect and correlate with their self-reported pain levels or medication effectiveness [3].

2. Integration of Physiological and Wearable Data

AI excels at finding patterns in complex, continuous data streams that would be invisible to the human eye. Wearable devices, already common for fitness tracking, are becoming powerful pain-tracking tools when paired with AI.

By fusing these data points, AI creates a digital biomarker for pain, offering a more reliable and continuous measure than a single daily self-report.

Practical Steps for Individuals to Use AI for Pain Tracking

For the general public and professionals interested in digital health, using AI for personal pain tracking is increasingly accessible:

1. Choose an AI-Enabled App: Start with a dedicated pain tracking app that incorporates AI or machine learning (e.g., Manage My Pain, Bearable). These apps go beyond simple diary entries by analyzing correlations between your logged data (pain score, mood, food, weather, medication) and identifying personalized triggers and patterns.
2. Integrate Wearable Data: Connect your pain tracking app to data from your smartwatch or fitness tracker. The AI in the app can then use your physiological data (HRV, sleep, steps) to validate and enrich your self-reported pain entries.
3. Utilize Predictive Analytics: The most advanced AI pain trackers use your historical data to predict when your pain is likely to worsen. This allows for proactive intervention, such as adjusting activity levels or taking preventative medication, rather than reactive treatment.
4. Share Insights with Clinicians: The AI-generated reports—which often include visualizations of pain patterns, trigger correlations, and treatment efficacy—provide clinicians with objective, longitudinal data, leading to more informed treatment decisions.

Ethical Considerations and the Future of AI in Pain Medicine

While the potential is vast, the use of AI in pain tracking is not without its challenges. Ethical concerns revolve around data privacy, algorithmic bias (ensuring the AI works equally well across all demographics), and the risk of over-reliance on technology [5]. AI should serve as a powerful assistive tool, not a replacement for the human-patient relationship.

The future of AI in pain tracking is moving toward highly personalized, preventative medicine. AI will not only track pain but also predict treatment response, helping to select the most effective therapy—whether it be a specific medication, physical therapy regimen, or psychological intervention—for an individual patient [6].

For more in-depth analysis on this topic, including the regulatory landscape and the integration of AI into clinical practice, the resources at www.rasitdinc.com provide expert commentary and further professional insight.

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References

[1] Zhang, M., Zhu, L., Lin, S. Y., et al. (2023). Using artificial intelligence to improve pain assessment and pain management: a scoping review. Journal of the American Medical Informatics Association, 30(3), 570–587. https://pmc.ncbi.nlm.nih.gov/articles/PMC9933069/ [2] PainChek. (n.d.). PainChek® Universal Pain Management App: How It Works. https://www.painchek.com/product/how-it-works/ [3] El-Tallawy, S. N., Pergolizzi, J. V., Vasiliu-Feltes, I., et al. (2024). Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review. Pain and Therapy. https://link.springer.com/article/10.1007/s40122-024-00584-8 [4] Ayena, J. C., et al. (2025). Predicting chronic pain using wearable devices: a scoping review. Frontiers in Digital Health. https://pmc.ncbi.nlm.nih.gov/articles/PMC12137249/ [5] Srinivasan, B. (2024). Artificial intelligence and pain management: cautiously optimistic. Expert Review of Neurotherapeutics, 24(10), 1059-1061. https://www.tandfonline.com/doi/full/10.1080/17581869.2024.2392483 [6] Casarin, S., et al. (2024). Transforming personalized chronic pain management with artificial intelligence. European Journal of Pain. https://www.sciencedirect.com/science/article/pii/S0014488624003066