ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2026
[Current Status of Multidisciplinary Team Consultation for Lung Cancer and Prospects under the Empowerment of Medical Large Models].
Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Lung cancer ranks among malignant neoplasms with the highest incidence and mortality, and its diagnosis and treatment are complicated, requiring multidisciplinary participation. Multidisciplinary team (MDT) consultation is the core model for modern lung cancer management. By pooling the expertise of specialists from diverse disciplines, MDT develops optimal individualized treatment regimens for patients and markedly improves diagnostic and therapeutic quality as well as patient prognosis. This article systematically summarizes the organizational framework, standardized procedures and clinical value of lung cancer MDT, alongside key problems in its popularization including obstacles in data integration, inconsistent decision-making efficiency and imbalanced resource allocation. It elaborates on the innovative effect of new technologies represented by artificial intelligence large language models on conventional MDT modes, and analyzes their application value in high-efficiency integration of multimodal medical data, real-time evidence-based decision-making support, optimization of consultation procedures and resources, as well as precise individualized treatment, so as to furnish theoretical basis and development ideas for establishing a new-generation intelligent, efficient and precise lung cancer MDT platform. .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.