Integrating Artificial Intelligence into Multidisciplinary Thoracic Oncology: From Diagnosis to Precision Therapy

Artificial Intelligence (AI) is increasingly revolutionizing thoracic oncology by enabling new ways to diagnose, treat, and monitor patients with thoracic malignancies. Within this dynamic field, AI technologies such as machine learning, radiomics, and deep learning algorithms have made it possible to improve screening methods, imaging interpretation, molecular characterization, and individualized treatment planning. Recent studies have highlighted the value of AI in refining risk prediction models, guiding surgical strategies, and optimizing radiation and chemotherapy regimens. Despite these advances, major questions persist regarding validation, interpretability, and integration into clinical workflows. The field still lacks comprehensive frameworks for ethical data use, unbiased algorithmic performance, and cross-specialty collaboration necessary for safe and effective adoption.This Research Topic aims to bring together researchers and clinicians from pulmonology, thoracic surgery, medical and radiation oncology, radiology, pathology, and data science to assess and advance the integration of AI into thoracic oncology. The goal is to evaluate how AI can enhance the accuracy of diagnosis, improve the precision of therapeutic decisions, and bridge existing gaps between technological innovation and clinical implementation. Key questions include how AI can support multidisciplinary decision-making, improve patient outcomes, and transform complex data into actionable insights across different stages of cancer care.To gather further insights within this multidisciplinary scope, we welcome contributions that address the application, optimization, and clinical translation of AI-driven methods in thoracic oncology. Articles may explore the following themes:- AI-assisted imaging, radiomics, and automated detection tools- Machine learning models for early diagnosis, staging, and outcome prediction- Integration of pathological, molecular, and clinical data for personalized treatment- AI applications in surgical planning, robotics, and minimally invasive procedures- Monitoring, response assessment, and adaptive therapy strategies- Digital health platforms and AI-based decision support systems-Ethical, regulatory, and data-governance challenges in clinical deployment Topic editors: Firas Abu Akar, Andres Obeso Source: https://www.frontiersin.org/research-topics/78730/integrating-artificial-intelligence-into-multidisciplinary-thoracic-oncology-from-diagnosis-to-precision-therapy