Artificial intelligence for clinical oncology
Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer
care. With recent advances in the field of artificial intelligence (AI), there is now a …
care. With recent advances in the field of artificial intelligence (AI), there is now a …
Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment
C Zhang, J Xu, R Tang, J Yang, W Wang, X Yu… - Journal of Hematology & …, 2023 - Springer
Research into the potential benefits of artificial intelligence for comprehending the intricate
biology of cancer has grown as a result of the widespread use of deep learning and …
biology of cancer has grown as a result of the widespread use of deep learning and …
Clinically applicable deep learning for diagnosis and referral in retinal disease
The volume and complexity of diagnostic imaging is increasing at a pace faster than the
availability of human expertise to interpret it. Artificial intelligence has shown great promise …
availability of human expertise to interpret it. Artificial intelligence has shown great promise …
Application of machine learning in predicting survival outcomes involving real-world data: a sco** review
Background Despite the interest in machine learning (ML) algorithms for analyzing real-
world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common …
world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common …
[HTML][HTML] Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
[HTML][HTML] Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques
Lung cancer is characterized by the uncontrollable growth of cells in the lung tissues. Early
diagnosis of malignant cells in the lungs, which provide oxygen to the human body and …
diagnosis of malignant cells in the lungs, which provide oxygen to the human body and …
An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT
J Zhou, B Hu, W Feng, Z Zhang, X Fu, H Shao… - NPJ digital …, 2023 - nature.com
Lung cancer screening using computed tomography (CT) has increased the detection rate of
small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically …
small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically …
A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability
Lung cancer, one of the deadliest forms of cancer, can significantly improve patient survival
rates by 60–70% if detected in its early stages. The prediction of lung cancer patient survival …
rates by 60–70% if detected in its early stages. The prediction of lung cancer patient survival …
Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer
There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop
an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This …
an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This …
[HTML][HTML] Predicting lung cancer survival based on clinical data using machine learning: A review
Abstract Machine learning has gained popularity in predicting survival time in the medical
field. This review examines studies utilizing machine learning and data-mining techniques to …
field. This review examines studies utilizing machine learning and data-mining techniques to …