About this Research Topic
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 to predict outcomes, eliminating the need for explicit programming. A recent review on 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 welcomes articles detailing the potential progression of AI, big data, and ML as tools within personalized medicine, health care, oncology, and thoracic surgery, serving diagnostic and therapeutic aims.
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 to predict outcomes, eliminating the need for explicit programming. A recent review on 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 welcomes articles detailing the potential progression of AI, big data, and ML as tools within personalized medicine, health care, oncology, and thoracic surgery, serving diagnostic and therapeutic aims.
Keywords: Thoracic Surgery, Surgical Oncology, Personalized Medicine, Big Data, Artificial Intelligence, Machine Learning
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