Artificial intelligence (AI) and machine learning (ML) are significantly transforming the medical landscape, enhancing diagnosis accuracy, surgical precision, and clinical outcomes. AI models intelligent human-like tasks, while ML facilitates the ongoing advancement of these computers’ intelligence. Leveraging the combination of AI and ML may enable medical professionals to detect bodily characteristics with greater precision, enhance strategic feedback, thereby improving medical and surgical efficiency and effectiveness. In fact, ML has already been implemented as a diagnostic tool to define complex pathological patterns. The synergy between AI and ML will inevitably be important towards driving the innovation of new machines and tools that will improve patients’ prognoses, survival rates, and quality of life following thoracic surgery, especially relating to thoracic oncology. In particular, AI has demonstrated promising results on patient prognosis focusing on surgical patient management because it implies not only the usefulness of the proposed surgical plan, but also the impact on the patients’ short- and long-term outcomes. Moreover, AI introduces new perspectives and possibilities in radiology, pathology, and respiratory medicine, which are important parts of the pre-operative, post-operative, and follow-up management of a patient. Meanwhile, ML portrays an AI dimension that allows applications to improve their accuracy in predicting outcomes, eliminating the need for explicit programming.
A recent review of 19 articles revealed varying perspectives on ML’s application, advantages and limitations, and its future specifically within lung surgery. This review highlighted how the field of lung surgery attempted to integrate ML algorithms and pinpointed gaps in literature concerning ML application in this specialty. Additionally, AI utilizes big data in precision medicine to analyze large volumes of diverse health-related data, for example, exploring novel genotype and phenotype data. Precision medicine aims towards early diagnosis, screening, and personalized treatment strategies based on genetic-oriented features and characteristics, recognizing patients with less common treatment responses. Recent studies indicate that translational research investigating the convergence between AI and precision medicine can address the challenging issues facing precision medicine and personalized healthcare. The necessity for future research in this field remains evident.
This Research Topic aims to explore the integration of AI and ML in thoracic surgery, particularly within the realm of thoracic oncology, to enhance patient outcomes through personalized medicine. The main objectives include investigating how AI and ML can improve diagnostic accuracy, surgical planning, and post-operative care. Specific questions to be addressed include: How can AI-driven tools enhance the precision of thoracic surgeries? What are the potential benefits and limitations of ML algorithms in predicting patient outcomes? How can big data be leveraged to develop personalized treatment strategies in thoracic oncology?
To gather further insights into the intersection of AI, ML, and thoracic surgery, we welcome articles addressing, but not limited to, the following themes:
• The role of AI in pre-operative planning and intra-operative decision-making in thoracic surgery;
• ML algorithms for predicting surgical outcomes and patient prognosis in thoracic oncology;
• Integration of big data analytics in developing personalized treatment plans;
• Advances in AI-driven diagnostic tools for early detection of thoracic malignancies;
• Case studies demonstrating the impact of AI and ML on patient care and surgical outcomes;
• Ethical considerations and challenges in implementing AI and ML in thoracic surgery;
• Comparative studies on the effectiveness of AI and ML tools versus traditional methods in thoracic surgery.
Keywords: Thoracic Surgery, Surgical Oncology, Personalized Medicine, Big Data, Artificial Intelligence, Machine Learning
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.
