In the era of big data, the proliferation of data types, dimensions, and volumes has catalyzed advancements in artificial intelligence. This technology now offers novel approaches and tools for medical diagnostics. By deeply mining and learning from the multimodal big data of diseases, artificial intelligence can uncover potential diagnostic evidence and plausible pathological mechanisms from diverse data sources. This assists physicians in diagnosing diseases, enhances diagnostic accuracy and efficiency, elucidates potential disease pathogenesis, and reduces diagnostic costs.

In primary healthcare settings, pulmonary chronic diseases including Chronic Obstructive Pulmonary Disease (COPD), Interstitial lung disease (ILD) and acute exacerbation conditions including Acute Respiratory Distress Syndrome (ARDS), Acute Chronic Obstructive Pulmonary Disease (AECOPD) and Pulmonary Embolism are marked by high prevalence, while infectious disease such as Tuberculosis are marked by rapid transmission, varied contagion modes, high infectivity, and quick mutation, frequently culminating in regionally focused outbreaks. Nevertheless, disparities in medical resources across regions are profound. These disparities often lead to challenges in affected regions, such as problematic large-scale community screenings, delayed medical services, escalated medical costs, diminished diagnostic precision, and compromised patient privacy during pulmonary disease outbreaks.

In response to these challenges, this special issue calls for high-quality contributions from academia and industry experts in big data, data fusion, machine learning, and artificial intelligence. The goal is to showcase cutting-edge methods and applications of AI technology integrated with multimodal data that enhance primary healthcare quality, particularly in community-based treatment of pulmonary diseases. Proposed submissions should be original, unpublished, and novel for in-depth research. Topics of interest for this special issue include, but not limited to:
● Large language model for Pulmonary Diseases
● Large Vision Model for Pulmonary Diseases
● Big Data Fusion and Its Application for Pulmonary Diseases
● Multimodal Methods for Early Pulmonary Diseases Screening
● Early Warning of Pulmonary Diseases Risk Methods
● Large language model for Primary Healthcare
● Large Vision Model for Primary Healthcare
● Big Data Fusion and Its Application for Primary Healthcare
● Medical Image Analysis Methods
● Incomplete Multimodal Theory and Methods for Medicine
● Mismatched multimodal Theory and Methods for Medicine
● Design and Implementation of Medical Virtual Assistants Methods
● Prediction of Drug Response Methods
● Design of Clinical Treatment Plans Methods
● Unsupervised Learning Methods for Medicine
● Supervised Learning Methods for Medicine
● Mixed-Supervised Learning for Medicine
● Semi-Supervised Learning for Medicine
● Comparative learning for Medicine

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Frontiers in Medicine

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