Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Despite unprecedented opportunities at the convergence of artificial intelligence (AI) and cancer research, few scientists possess fluency in both domains. We propose a six-principle framework for training "AI-oncology bilingual" scientists who can bridge this gap and translate AI-driven discoveries into improved patient outcomes.
BACKGROUND: To identify novel periodontal phenotypes using unsupervised machine learning on a large-scale, multicenter cohort, specifically characterizing disease patterns based on the "periodontal architecture" of localized structural failures (tooth mobility and molar furcation defects) rather than global severity averages alone. METHODS: This cross-sectional study analyzed electronic health rec...
PURPOSE: To evaluate the value of integrating habitat radiomics features and deep learning features for predicting occult lymph node metastasis (OLNM)...
OBJECTIVE: Recently, deep learning (DL)-based reconstruction methods have been introduced into clinical magnetic resonance imaging (MRI) systems to en...
BACKGROUND: Breast cancer affects millions of women and presents not only medical challenges but also emotional, financial, and social burdens. Beyond...
Generative design and machine learning are increasingly prevalent in medicinal chemistry. To pilot the comprehensive use of automated molecular design...
BACKGROUND: Optimal patient selection for the most effective BTK inhibitor (BTKi) partner of venetoclax in fixed-duration (FD) BTKi-venetoclax regimen...
Tailoring cancer treatment to the primary tumor site is essential for optimal outcomes, yet identifying the source of metastasis remains a significant...
BACKGROUND: Implant-based reconstruction failure remains a significant complication following breast reconstruction, with substantial implications for...
Chemotherapy-induced madarosis significantly impacts patient quality of life, yet current assessment methods rely heavily on subjective grading, limit...
Hepatocellular carcinoma (HCC) is a highly lethal malignancy with high invasiveness and metastasis. Despite progress in its treatment, the high mortal...
Early detection of pancreatic cancer remains a critical challenge in oncology, with current diagnostic methods often failing to identify the disease u...
CONTEXT: Chatbots are increasingly used by the public, but their performance in answering questions about complex health topics, such as cannabis, is ...
Personalized cancer vaccines have re-emerged as a promising strategy in precision immunotherapy, driven by advances in tumor sequencing, neoantigen id...
BACKGROUND AND OBJECTIVE: Actinic Keratosis (AK) is a common skin condition, usually appearing on sun-exposed areas, whose progression is associated w...
OBJECTIVE: This review critically evaluates the diagnostic potential of salivary biomarkers in oral diseases, highlighting their role as non-invasive ...
Digital twin technology has emerged as a transformative innovation in healthcare, offering virtual replicas of physical entities at patient-level, equ...
BACKGROUND: The precise prognostic stratification of intrahepatic cholangiocarcinoma (iCCA) remains challenging. We aimed to develop and validate inte...
Early and reliable detection of brain tumors from magnetic resonance imaging (MRI) is essential for timely clinical diagnosis and treatment planning. ...
BACKGROUND: Artificial intelligence (AI) technologies, particularly large language models (LLMs) such as ChatGPT, are increasingly utilised in medical...