Artificial Intelligence and the Language of Chronic Pain: From Patient Discourse and Clinical Records to Understanding and Care

Chronic pain, whether arising from musculoskeletal, inflammatory, neuropathic, visceral, or idiopathic conditions, is a leading cause of disability worldwide, yet it remains difficult to measure objectively. Because pain is a subjective and private experience, language is a primary means through which patients communicate its quality, intensity, temporal pattern, and psychosocial impact, and through which clinicians document their assessments. Recent advances in artificial intelligence (AI), particularly natural language processing and large language models (LLMs), have created new opportunities to analyse pain-related discourse at scale. Emerging studies suggest that computational methods can identify underlying pathology from verbal pain descriptions, characterise chronic pain experiences using large online datasets, and support the prediction of clinical outcomes, including treatment and placebo responses. Generative and agentic AI may also assist clinical assessment, improve communication, and simulate pain experiences to support education, empathy, and professional training. However, important questions remain regarding the validity, interpretability, generalisability, fairness, and clinical usefulness of these approaches.This Research Topic aims to consolidate and advance the use of AI to understand, assess, and manage chronic pain across diverse aetiologies. It will bring together researchers using AI to integrate complementary sources of evidence, including patient discourse from interviews, questionnaires, and social media, as well as clinical information from medical records, referrals, and structured assessments. Contributions may examine how language and multimodal models can characterise pain experiences, identify biopsychosocial factors, predict disease and pain trajectories, support treatment selection, or improve clinical workflows. We are also interested in conversational systems that assist patients in completing complex instruments, such as the McGill Pain Questionnaire, and generative applications that simulate pain experiences for education and clinician training. Particular emphasis will be placed on transparent reporting, independent validation, clinical relevance, and the ethical, fair, and privacy-preserving deployment of AI in pain research and care.To gather further insights into the development, evaluation, and clinical translation of AI for chronic pain, we welcome articles addressing, but not limited to, the following themes:Natural language processing and LLMs for pain assessment using patient discourse and clinical documentationAI-based prediction of underlying pathology, pain trajectories, treatment response, and placebo responseMultimodal AI integrating language with imaging, sensor, physiological, or behavioural dataAgentic and conversational AI for questionnaire completion, symptom monitoring, patient communication, and clinical documentationGenerative AI for pain simulation, education, empathy development, and clinician trainingDataset creation, annotation, benchmarking, reproducibility, and external validation in computational pain researchInterpretability, explainability, fairness, privacy, governance, and ethical challenges in AI-enabled pain careEvaluation of clinical utility, implementation barriers, and human–AI collaboration in pain assessment and managementAI applications across musculoskeletal, inflammatory, neuropathic, visceral, and idiopathic pain conditionsRepresentation of diverse populations and approaches to mitigating bias in pain-related datasets and modelsWe welcome Original Research, Reviews, Methods, Perspectives, Data Reports, and Brief Research Reports. Submissions should clearly report data provenance, validation strategies, limitations, and the potential clinical relevance of the proposed approach. Topic editors: Joana Ferreira-Gomes, Paulo Branco, Fani Moreira Neto, David Martins de Matos Source: https://www.frontiersin.org/research-topics/83548/artificial-intelligence-and-the-language-of-chronic-pain-from-patient-discourse-and-clinical-records-to-understanding-and-care