Can AI determine a perceived pain level by monitoring bodily metrics or brain activity ?

How can artificial intelligence translate body signals into a real-time estimate of how much pain a person is feeling? Researchers have begun combining heartbeats, skin responses, facial cues and brain scans with machine learning in an attempt to build an objective window into subjective suffering, particularly for patients who cannot describe their pain themselves.

Background

AI systems currently estimate perceived pain levels by processing multimodal physiological data such as heart rate variability, skin conductance, facial expressions and central nervous system activity captured by electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) [Nature Biomedical Engineering, 2023]. These pipelines typically involve supervised machine-learning models trained on datasets that pair raw biosignals with self-reported pain scores (e.g., 0–10 numeric rating scales) to learn predictive mappings between bodily metrics and subjective discomfort. Studies report correlations between biomarker shifts and pain ratings in both acute experimental settings and chronic clinical cohorts, suggesting a measurable physiological signature of pain that can be quantified even when verbal reports are unavailable. Challenges include pronounced inter-individual variability (age, medication, baseline autonomic tone), strong context dependence (pain type, emotional state, environmental triggers), and the irreducible subjectivity of the pain experience. Recent work therefore emphasizes multimodal fusion, domain adaptation, and causal interpretability techniques to improve robustness and clinical translatability.