Reshaping Precision Cancer Pain Management with Multimodal Artificial Intelligence: A Review on Subtyping and...

Area of Science:

• Oncology
• Pain Medicine
• Artificial Intelligence

Background:

• Cancer pain is highly heterogeneous, making standard treatments often ineffective.
• There is a critical need for personalized strategies to subtype pain and predict treatment efficacy.

Purpose of the Study:

• To propose a patient-specific digital twin framework powered by Artificial Intelligence (AI) for personalized cancer pain management.
• To outline a research roadmap and implementation model for clinical translation.

Main Methods:

• Utilizing multimodal AI, including large foundation models and transformer architectures.
• Integrating diverse data streams: genomics, digital pathology, clinical text, and patient-reported outcomes.
• Developing a framework to classify pain subtypes (nociceptive, neuropathic, nociplastic) and predict analgesic sensitivity.

Main Results:

• The AI-driven digital twin can uncover complex patterns to simultaneously subtype cancer pain and predict individual responses to analgesics.
• Identified key translational barriers including data scarcity, model interpretability, validation needs, and privacy concerns.

Conclusions:

• An AI-powered digital twin offers a transformative approach to precise, dynamic, and personalized cancer pain management.
• A hybrid telemedicine and on-site expert model is proposed for clinical implementation to improve patient outcomes worldwide.