About this Research Topic
In recent years, the rapid development of artificial intelligence (AI) and machine learning (ML) has significantly influenced the management of lung diseases, which remain a leading cause of morbidity and mortality worldwide. The accuracy and efficiency of diagnosis, treatment planning, and outcome prediction could be significantly improved by integrating AI and ML into clinical practice.
AI-driven approaches are increasingly being utilized to analyze diverse data sources, including but not limited to imaging, pathology, genetic, and clinical data. Utilizing these extensive and diverse datasets, AI models can create predictive tools that tailor treatment strategies to individuals, supporting the overarching objectives of precision medicine. This data-driven approach supports clinicians in making more informed decisions, improving the accuracy and timeliness of patient care. The focus of this Research Topic is to explore the forefront of AI applications in the comprehensive diagnosis and treatment of lung diseases. This includes not only predictive assistance but also the broader integration of AI into clinical workflows, setting the stage for innovative and personalized approaches in lung care.
This Research Topic aims at addressing the critical challenges in the diagnosis and treatment of lung diseases through the innovative application of machine learning (ML). Specifically, it seeks to explore how AI-driven methods can be harnessed to analyze diverse and complex data sources, including imaging, pathology, genetic, and clinical data, to develop predictive models that enhance clinical decision-making. The goal is to foster interdisciplinary research that bridges traditional clinical practices with cutting-edge AI technologies, ultimately advancing the precision and personalization of lung care. By bringing together experts from various fields, this Research Topic aspires to generate new insights, methodologies, and tools that can improve patient outcomes and set the stage for the next generation of AI applications in comprehensive lung disease management.
This Research Topic invites contributions on a range of innovative themes in lung disease management, focusing on the integration of artificial intelligence and machine learning. Key areas of interest include:
• Integrating radiomic and genomic data to enhance precise diagnosis and treatment for lung diseases.
• Leveraging AI technologies for the prediction and prognostic modeling of lung diseases to improve patient outcomes.
• Combining multi-modal and multi-omics data to enable personalized medical decision-making and risk assessment for lung diseases.
• Utilizing AI for real-time surgical planning and risk stratification to increase the safety and effectiveness of surgeries for lung diseases.
• Applying deep learning for pathology image analysis and dynamic risk assessment to optimize treatment plans for lung diseases.
We encourage submissions that explore these themes, aiming to advance the precision, personalization, and overall effectiveness of lung care.
AI-driven approaches are increasingly being utilized to analyze diverse data sources, including but not limited to imaging, pathology, genetic, and clinical data. Utilizing these extensive and diverse datasets, AI models can create predictive tools that tailor treatment strategies to individuals, supporting the overarching objectives of precision medicine. This data-driven approach supports clinicians in making more informed decisions, improving the accuracy and timeliness of patient care. The focus of this Research Topic is to explore the forefront of AI applications in the comprehensive diagnosis and treatment of lung diseases. This includes not only predictive assistance but also the broader integration of AI into clinical workflows, setting the stage for innovative and personalized approaches in lung care.
This Research Topic aims at addressing the critical challenges in the diagnosis and treatment of lung diseases through the innovative application of machine learning (ML). Specifically, it seeks to explore how AI-driven methods can be harnessed to analyze diverse and complex data sources, including imaging, pathology, genetic, and clinical data, to develop predictive models that enhance clinical decision-making. The goal is to foster interdisciplinary research that bridges traditional clinical practices with cutting-edge AI technologies, ultimately advancing the precision and personalization of lung care. By bringing together experts from various fields, this Research Topic aspires to generate new insights, methodologies, and tools that can improve patient outcomes and set the stage for the next generation of AI applications in comprehensive lung disease management.
This Research Topic invites contributions on a range of innovative themes in lung disease management, focusing on the integration of artificial intelligence and machine learning. Key areas of interest include:
• Integrating radiomic and genomic data to enhance precise diagnosis and treatment for lung diseases.
• Leveraging AI technologies for the prediction and prognostic modeling of lung diseases to improve patient outcomes.
• Combining multi-modal and multi-omics data to enable personalized medical decision-making and risk assessment for lung diseases.
• Utilizing AI for real-time surgical planning and risk stratification to increase the safety and effectiveness of surgeries for lung diseases.
• Applying deep learning for pathology image analysis and dynamic risk assessment to optimize treatment plans for lung diseases.
We encourage submissions that explore these themes, aiming to advance the precision, personalization, and overall effectiveness of lung care.
Keywords: Machine Learning, Radiomics, Cardiothoracic Diseases, Precision Medicine, Predictive Assistance
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
