Application of AI and multimodal fusion in the differential diagnosis of benign and malignant pulmonary nodules

Objective  To systematically review recent advances in artificial intelligence (AI) and multimodal fusion for differentiating benign from malignant pulmonary nodules, with a focus on the theoretical basis, key technologies, clinical utility, and practical boundaries of integrated decision-making based on imaging, clinical data, and blood-based biomarkers. Methods　International guidelines for pulmonary nodule management, classic risk prediction models, recent AI-based imaging studies, multi-omics and liquid biopsy studies, and methodological consensus documents were reviewed. Evidence was synthesized from six perspectives: the significance of multimodal assessment, integration of imaging and clinical variables, synergistic value of blood biomarkers including ctDNA and circulating genetically abnormal cells (CAC), clinical potential of multimodal models, the boundary between decision support and decision replacement, and current challenges with possible solutions. Results　Current pulmonary nodule management still relies primarily on nodule size, volume, density, margin characteristics, growth dynamics, and conventional clinical risk factors such as age, smoking history, and prior malignancy, under the framework of established guidelines and prediction models. However, in subcentimeter nodules, subsolid nodules, multiple nodules, inflammation-related nodules, and intermediate-risk nodules, single-modality imaging features and conventional models remain inadequate in calibration and net clinical benefit. AI-based radiomics, deep learning, and multimodal machine learning can extract high-dimensional CT features beyond human visual recognition and improve risk stratification when combined with clinical variables. Meanwhile, liquid biopsy approaches, including cfDNA/ctDNA methylation, fragmentomics, CAC, and proteomic classifiers, provide additional molecular and cellular evidence for intermediate-risk nodules, thereby helping reduce unnecessary invasive procedures and accelerating precision diagnosis in truly high-risk cases. Nevertheless, real-world implementation remains limited by data heterogeneity, insufficient external validation, lack of assay standardization, high-dimensional low-sample-size issues, and regulatory and reimbursement barriers. Conclusion　The differential diagnosis of pulmonary nodules is evolving from single-modality imaging judgment toward multimodal integrated decision-making based on imaging, clinical data, and biomarkers. At the current stage, AI should be positioned as a decision-support tool rather than a decision-replacement tool. Future practice-changing systems will likely be prospectively validated, interpretable, auditable, guideline-concordant multimodal platforms that can be seamlessly embedded into pulmonary nodule clinics and multidisciplinary workflows.

Key Words: pulmonary nodule; artificial intelligence; multimodal fusion; radiomics; deep learning; cfDNA methylation; circulating genetically abnormal cells; proteomics; decision support