ReviewAdvances in experimental medicine and biology2024
Machine Learning in Pain Neuromodulation.
Review in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Emerging Nonpharmacologic Analgesic Technologies in Anesthesia: Mechanisms, Evidence, and Future Directions for Pharmacologic Alternatives.Biomedicines · 2026Review
- FPGA-Enabled, Smart Implants: A Path to Personalized Healthcare.IEEE open journal of engineering in medicine and biology · 2026Article
- Neural mechanisms and neuromodulation therapies for non-suicidal self-injury.Molecular psychiatry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This chapter highlights the intersection of pain neuromodulation and machine learning (ML), exploring current limitations in pain management and how ML techniques can address these challenges. Neuromodulation technologies, such as spinal cord stimulation (SCS), have emerged as promising interventions for chronic pain, but limitations such as patient selection have resulted in high rates of failure and costly removal of these devices. ML offers a powerful approach to augment pain management outcomes by leveraging predictive modeling for enhanced patient selection, adaptive algorithms for programming optimization, and identification of objective biomarkers for improved outcome assessment. This chapter discusses various ML applications in pain neuromodulation and how we can expect it to shape the future of the field. While ML holds great promise, challenges such as algorithm transparency, data quality, and generalizability must be addressed to fully realize its potential in revolutionizing pain management.
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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.