AIMC Topic: Neoplasms

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The use of large language models to enhance cancer clinical trial educational materials.

JNCI cancer spectrum
BACKGROUND: Adequate patient awareness and understanding of cancer clinical trials is essential for trial recruitment, informed decision making, and protocol adherence. Although large language models (LLMs) have shown promise for patient education, t...

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.

Cancer medicine
INTRODUCTION: Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetua...

[Prophylactic surgery and genetic counselling: What impact of the artificial intelligence?].

Bulletin du cancer
In the area of cancer predisposition, certain situations may lead to the discussion of prophylactic surgery. This is rarely strictly recommended and depends on the patient's choice. The advantages and disadvantages must be weighed up. The main advant...

Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients.

Cancer medicine
BACKGROUND: There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions...

An audit of the impact of the introduction of a commercial artificial intelligence-driven auto-contouring tool into a radiotherapy department.

The British journal of radiology
OBJECTIVES: To audit prospectively the accuracy, time saving, and utility of a commercial artificial intelligence auto-contouring tool (AIAC). To assess the reallocation of time released by AIAC.

Uncertainty CNNs: A path to enhanced medical image classification performance.

Mathematical biosciences and engineering : MBE
The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient model...

Recurrent Neural Network/Machine Learning Predictions of Reactive Channels in H + CH at E = 30 eV: A Prototype of Ion Cancer Therapy Reactions.

Journal of computational chemistry
We present a simplest-level electron nuclear dynamics/machine learning (SLEND/ML) approach to predict chemical properties in ion cancer therapy (ICT) reactions. SLEND is a time-dependent, variational, on-the-fly, and nonadiabatic method. In SLEND, nu...

Building a cancer risk and survival prediction model based on social determinants of health combined with machine learning: A NHANES 1999 to 2018 retrospective cohort study.

Medicine
The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, researchers have identified that social determinants of health (SDOH) (employment status, family inc...

The Hallmarks of Predictive Oncology.

Cancer discovery
As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks,...

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

American journal of epidemiology
There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and met...