Multimorbidity has emerged as a central challenge to global healthcare systems in the context of global population aging. This article systematically examines the applicability challenges and optimization strategies of clinical practice guideline recommendations in multimorbidity management. Most existing guidelines remain pre-dominantly single-disease-oriented, leading to conflicts between recommendations, limited evidence generalizability, increased decision-making complexity, delayed guideline updates, and implementation difficulties in real-world settings when applied to complex comorbid conditions. To address these challenges, this study proposes methodological innovations in guideline development through incorporating "multimorbidity consider-ations," integrating real-world evidence, and advancing computable and modular transformation of guidelines to enhance their relevance and adaptability. Additionally, establishing mechanisms for resolving recommendation conflicts, combined with multi-dimensional analysis and artificial intelligence-assisted decision-making, can facilitate personalized treatment recommendations. In clinical practice, strategies such as multi-criteria decision analysis, deprescribing, "one-drug-multiple-diseases" approaches, and integrated traditional Chinese and Western medicine pathways can optimize treatment for patients with comorbidities. Future efforts should prioritize patient-centered guideline development and promote the translation of evidence into practice to improve the quality and efficiency of multimorbidity management.

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