The application of robots in thoracic surgery is mainly based on the da Vinci general surgery robot. With the popularization of artificial intelligence (AI) application scenarios, the combination of AI and robots is more closely, and there is a strong clinical demand and huge application space for the development of specialized disease-specific robotic systems for thoracic surgery. This article aims to systematically describe the history of the rise of specialized surgical robots and the status of the localization of surgical robots in China, propose the concept of applying AI to the research and development of integrated specialized disease-specific robots in thoracic surgery, and clarify the ethics and prospects that intelligent specialized disease-specific surgical robots will face.
ObjectiveTo construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.
With the increasing use of low-dose computed tomography (LDCT) in lung cancer screening and health examinations, the clinical goal of pulmonary nodule management has shifted from detecting nodules to identifying lesions that warrant intervention while avoiding overdiagnosis and overtreatment of low-risk disease. Artificial intelligence (AI) has consequently evolved from computer-aided detection and segmentation to benign-malignant differentiation, longitudinal growth assessment, invasiveness prediction, risk stratification, and closed-loop workflow support. This review outlines the developmental trajectory of AI for pulmonary nodules and summarizes advances in detection and segmentation, radiomics, end-to-end deep learning, longitudinal modeling, and multimodal integration across incidentally detected, screening-detected, subsolid, and multiple-nodule scenarios. Particular attention is given to real-world failure modes and their causes, including data and label bias, scanner- and protocol-related distribution shift, spectrum bias and overfitting in subsolid nodules, missed atypical lesions, automation bias, and management of discordance between AI outputs and guideline-based recommendations. Current evidence suggests that AI may improve detection efficiency and risk reclassification in selected settings; however, evidence regarding cross-population calibration, patient-relevant outcomes, cost-effectiveness, and post-deployment monitoring remains limited. AI should therefore be positioned as a human-in-the-loop adjunct within guideline-governed pathways, supported by scenario-specific validation, transparent reporting, interpretability and accountability, continuous performance auditing, and multidisciplinary decision-making.
