Artificial intelligence and machine learning techniques for predicting neuropathic pain in patients with cancer: A systematic review.

BACKGROUND

Neuropathic pain (NP) remains a complex, under-recognized complication among cancer patients, frequently arising from surgery, chemotherapy, or radiotherapy. Early prediction is crucial for timely intervention, yet conventional tools often fall short due to their reactive and subjective nature.

AIM

This systematic review aims to evaluate the application of artificial intelligence (AI) and machine learning (ML) techniques in predicting NP and related outcomes among oncology patients, highlighting model performance, predictors, and limitations.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a comprehensive search was conducted across PubMed, EMBASE, Web of Science, IEEE Xplore, and Google Scholar for English-language studies published between January 2020 and February 2025. Fourteen eligible studies were included based on predefined Population, Intervention, Comparator, Outcome, Study Design (PICOS) criteria. The risk of bias was assessed using QUADAS-2 and PROBAST tools.

RESULTS

Most studies in high-income countries focused on breast cancer. Supervised models such as random forest (area under the receiver operating characteristic curve (AUC) up to 0.94), support vector machine (AUC 0.808-0.87), and deep learning architectures were dominant. Key predictive features included acute postoperative pain, anxiety, type of surgery, and biomarkers like sphinganine-1-phosphate. Only 14% of studies used external validation, and 5% assessed calibration. Multimodal frameworks integrating clinical, emotional, imaging, and molecular data outperformed single-modality models.

CONCLUSION

AI and ML hold significant promise for enhancing NP prediction in cancer care. However, methodological limitations-particularly poor calibration, low external validation, and limited interpretability-currently hinder clinical adoption. Standardization, explainable AI, and diverse datasets are essential for future progress.