AIMC Topic: Precision Medicine

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Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes.

Expert opinion on drug delivery
INTRODUCTION: Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic a...

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Medical image analysis
Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the pred...

Comparative Analysis of Generative Pre-Trained Transformer Models in Oncogene-Driven Non-Small Cell Lung Cancer: Introducing the Generative Artificial Intelligence Performance Score.

JCO clinical cancer informatics
PURPOSE: Precision oncology in non-small cell lung cancer (NSCLC) relies on biomarker testing for clinical decision making. Despite its importance, challenges like the lack of genomic oncology training, nonstandardized biomarker reporting, and a rapi...

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.

Frontiers in immunology
INTRODUCTION: Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential for precision oncology.

Applications of and issues with machine learning in medicine: Bridging the gap with explainable AI.

Bioscience trends
In recent years, machine learning, and particularly deep learning, has shown remarkable potential in various fields, including medicine. Advanced techniques like convolutional neural networks and transformers have enabled high-performance predictions...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Trends in cardiovascular medicine
Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing signif...

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.

Journal of computational biology : a journal of computational molecular cell biology
Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have be...

[The future of medicine: an informed look into the "crystal ball"].

Deutsche medizinische Wochenschrift (1946)
This article explores potential future scenarios for the medical field based on current trends, technological advancements, and social dynamics. By examining advances in artificial intelligence, immersive technologies, genomics, and digital health in...

Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.

Journal of applied clinical medical physics
PURPOSE: Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment p...

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.

Frontiers in immunology
INTRODUCTION: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived...