AI in Lung Cancer: Diagnostic Imaging, Treatment Selection, and Prognostic Assessment
Published in Clinical Chemistry and Laboratory Medicine, this review surveys AI applications across the lung cancer care pathway, from early nodule detection through image recognition to treatment optimisation and prognostic modelling. The authors cover natural language processing, machine learning and computer vision methods, emphasising their potential to enhance diagnostic efficiency and reduce the complexity of associating early pulmonary nodules with neoplastic changes in clinical laboratory and pathology workflows.
The original study
Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis.
- Authors
- Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T
- Journal
- Clinical chemistry and laboratory medicine
- Type
- Journal Article, Review
- PMID
- 35771735
Original abstract
Artificial intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.