Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their outputs. Opioid prescribing is one domain in which these biases can have serious implications, especially given the ongoing opioid epidemic and the need to balance effective pain management with addiction risk. We tested ten LLMs—both open-access and...
Large-scale cohort studies exploring the etiology of obstructive jaundice (OJ) are scarce, with current serum-based diagnostic markers offering suboptimal performance. This study leverages the largest retrospective cohort of OJ patients to date to investigate its disease spectrum and to develop a novel diagnostic system. This study involves two retrospective observational cohorts. The biliary surg...
Pancreatic ductal adenocarcinoma (PDAC) is a rare, aggressive cancer often diagnosed late with low survival rates, due to the lack of population-wide ...
Machine learning (ML) applications within diagnostic histopathology have been extremely successful. While many successful models have been built using...
In the field of medical crowdfunding prediction, traditional statistical methods have long been the standard. Machine learning algorithms are popular ...
To synthesize existing literature on patient attitudes toward AI in cancer care and identify knowledge gaps that can inform future research and clinic...
Sleep quality is vital to human health, yet automated sleep staging faces challenges in cross-center generalization due to data scarcity and domain ga...
Accurate delineation of orodental structures on radiotherapy CT images is essential for dosimetric assessments and dental decisions. We propose a deep...
Breast cancer is one of the leading causes of cancer-related mortality among women worldwide. Despite advancements in treatment, therapeutic resistanc...
Clinical research is limited by the capability to define the most important combinations of clinical features and biomarkers that predict therapeutic ...
This study explores the multi-level regulatory roles of the lactate metabolism gene network in oral cancer development using machine learning models. ...
Neoantigens have emerged as promising targets for personalized cancer immunotherapy. However, accurate identification of immunogenic neoantigens remai...
Extracting structured data from free-text medical records at scale is laborious, and traditional approaches struggle in complex clinical domains. We p...
Multiple myeloma (MM) is the second most common hematologic malignancy in the U.S., with Black patients being diagnosed at twice the rate of White pat...
Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivabili...
A comprehensive analysis of artificial intelligence’s (AI) integration into neurosurgery is vital to identify research priorities, address gaps, and i...
T1w/T2w ratio mapping, combining voxel-wise signal intensities in T1-weighted (T1w) and T2-weighted (T2w) structural MRI, has been used to investigate...
Large language models (LLMs) have demonstrated advanced capabilities in interpreting text and visual inputs. Their potential to transform oncological ...
Liquid biopsies and cell-free DNA (cfDNA) offer minimally invasive methods for the diagnosis and monitoring of Ewing Sarcoma (EwS). EwS have a low tum...
Introduction: Achieving equity in translational precision medicine requires the integration of genomic, clinical, and social determinants of health (S...