AIMC Topic: Early Detection of Cancer

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Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) studies show promise in enhancing accuracy and efficiency in mammographic screening programs worldwide. However, its integration into clinical workflows faces several challenges, including unintended errors, t...

Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.

World journal of gastroenterology
Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperative to focus on strategies that can mitigate its impact. Post-treatment surveillance has emerged as essential for early detection of recurrence, signi...

M-GNN: A Graph Neural Network Framework for Lung Cancer Detection Using Metabolomics and Heterogeneous Graph Modeling.

International journal of molecular sciences
Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection critical for improving survival rates, yet conventional methods like CT scans often yield high false-positive rates. This study introduces M-GNN, a grap...

The Potential Role of AI- and Machine Learning Models in the Early Detection of Oral Cancer and Oral Potentially Malignant Disorders.

Studies in health technology and informatics
INTRODUCTION: Artificial Intelligence (AI) is playing an increasing role in advancing diagnostic processes and decision-making in healthcare. In the early detection of oral cancer and oral potentially malignant disorders (OPMDs), its role is still be...

A systematic review and meta-analysis of lung cancer risk prediction models.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.

Mobile health apps for skin cancer triage in the general population: a qualitative study on healthcare providers' perspectives.

BMC cancer
BACKGROUND: Mobile health (mHealth) applications (apps) integrated with artificial intelligence for skin cancer triage are increasingly available to the general public. Nevertheless, their actual uptake is limited. Although endorsement by healthcare ...

Binary classification of gynecological cancers based on ATR-FTIR spectroscopy and machine learning using urine samples.

Clinical and experimental medicine
Making an early diagnosis of cancer still in the early stages, when completely asymptomatic, is the challenge modern medicine has been setting for several decades. In gynecology, no effective screening has yet been found and approved for endometrial ...

Light Bladder Net: Non-invasive Bladder Cancer Prediction by Weighted Deep Learning Approaches and Graphical Data Transformation.

Anticancer research
BACKGROUND/AIM: Bladder cancer (BCa) is associated with high recurrence rates, emphasizing the importance of early and accurate detection. This study aimed to develop a lightweight and fast deep learning model, Light-Bladder-Net (LBN), for non-invasi...