Intelligent Detection for Pancreatic Cancer Diagnosis: Future Directions - Kister, Klaudia; Laskowski, Jakub; Mazur, Magdalena; Zach-Źródlak, Monika; Małolepsza, Aleksandra; Rektor, Natalia; Czechowska, Julia; Rosa, Lidia; Bronst, Paulina; Szabrańska, Anna (Journal of Education, Health and Sport, 2023)
Introduction: Artificial intelligence is one of the most modern information systems that bases its operation on analogous functioning to the human mind. Particular hopes are placed in the treatment of diseases considered incurable. One such disease is pancreatic cancer, which has an extremely poor prognosis. Thanks to the use of machine learning and deep learning, artificial intelligence analyzes the available images taken during computed tomography and compares them with the introduced changes characteristic of pancreatic cancer. The advantage of the machines is the ability to analyze data, scans and images in a fraction of a second. Material and methods: The authors reviewed the peer-reviewed international literature describing the application of artificial intelligence in oncology. Databases such as PubMed, SCOPUS and Web of were searched with the keywords "artificial intelligence", "oncology" and "pancreatic cancer". The review included articles from 2004-2023. Discussion: Pancreatic cancer is an important topic of research due to unsatisfactory treatment results and the lack of screening tests allowing for quick detection of people at risk. Artificial intelligence is becoming a promising solution for asymptomatic patients, who can be classified into risk groups much faster than traditional methods. Unlike humans, intelligent machines can work faster and continuously, delivering content and results much faster. Conclusions: Artificial intelligence is a promising tool in the diagnosis of pancreatic cancer. Research results indicate that it is the most widely used in the field of oncological radiology, thanks to which imaging of early stages of cancer is becoming easier to detect. There is a need to verify machine methods and avoid bias when privatizing smart machines. The good of the patient should always come first, especially in a disease with an unfavorable prognosis, such as pancreatic cancer.