Track 20: Artificial Intelligence for Disease Diagnosis, Prognosis, Cancer and Prevention

AI has emerged as a viable option for enhancing patient outcomes and healthcare accuracy. AI is already being used to predict and automate several malignancies. Risk assessment, early diagnosis, patient prognosis calculation, and treatment selection based on in-depth knowledge are among the applications of AI in oncology.

AI has emerged as a viable option for enhancing patient outcomes and healthcare accuracy. AI is already being used to predict and automate several malignancies. Risk assessment, early diagnosis, patient prognosis calculation, and treatment selection based on in-depth knowledge are among the applications of AI in oncology.

Cancer has a low median survival rate and is an aggressive disease. Ironically, because of the high rates of recurrence and mortality, the treatment regimen is time-consuming and expensive. To increase a patient’s chance of survival, cancer prognostic prediction and early detection must be accurate. Many scientists are now using computational techniques, like multivariate statistical analysis, to analyze the prognosis of the disease, and the accuracy of these analyses is much higher than that of empirical predictions, thanks to advancements in computer engineering and statistics over the years. Furthermore, cancer prediction performance has increased to unprecedented levels as artificial intelligence (AI), particularly machine learning and deep learning, has found widespread applications in clinical cancer research in recent years.

In the last several years, clinical cancer research has become increasingly reliant on artificial intelligence (AI), particularly machine learning and deep learning. As a result, prediction performance in this area has increased significantly.
Given its unparalleled accuracy level—which is even higher than that of conventional statistical applications in oncology—AI is known to aid in cancer diagnosis and prognosis.