Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends

Abstract

Artificial intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small-cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical record (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal the potential to improve surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and perioperative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

Keywords: AI; RATS; VATS; artificial intelligence; precision medicine; precision surgery; thoracic surgery.

Figure 1

Subcategories of artificial intelligence [5].

Figure 2

Process and steps in artificial intelligence [5]. * Unsupervised methods are used for pattern recognition and clustering rather than generating predictive models.