Culturally Responsive AI-Supported Cancer Pain Education: A Human-Centered Framework for Multilingual Oncology Care
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Abstract
Cancer pain education aims to help patients understand pain, communicate symptoms, and follow clinician-approved management plans; however, improved knowledge does not consistently translate into reduced pain intensity. Generative artificial intelligence (AI) is increasingly proposed for patient-facing education, while evidence supporting culturally and linguistically responsive oncology applications remains limited. This critical perspective develops a human-centered framework for AI-supported cancer pain education in multilingual oncology care. A focused, non-systematic critical synthesis integrated research on cancer pain education, health literacy, cultural adaptation, multilingual communication, and AI governance. No primary data were collected. The framework proposes six interdependent design domains: patient-led co-design and cultural humility; multilingual, accessible, dialogic learning; accurate pain science that avoids reductionist explanations; clearly bounded, evidence-grounded AI functions; explicit human oversight and escalation procedures; and lifecycle governance with equity-focused evaluation. These domains constitute conceptual recommendations requiring empirical evaluation. A staged evaluation pathway progresses from co-design and cognitive testing to offline AI benchmarking, supervised usability pilots, and comparative implementation studies. Initial evaluations should prioritize comprehension, communication, trust, safety, and access across language groups. Potential effects on pain interference and other clinical outcomes remain hypotheses for prospective testing. Illustrative examples involving Moroccan Darija highlight linguistic and cultural adaptation challenges without establishing cancer-specific validation. The proposed AI role is restricted to educational support and does not encompass diagnosis, prescribing, psychotherapy, professional interpretation, or replacement of oncology professionals. By connecting health education, cultural responsiveness, pain science, and responsible AI governance, the framework provides a basis for developing and evaluating multilingual educational support. Its contribution lies in specifying design and evaluation priorities while retaining clinical effectiveness as an open empirical question.
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