AIMC Topic: Medical Oncology

Clear Filters Showing 211 to 220 of 318 articles

Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches-such as CNNs and deep learning-have improved the detection and classification of brain tumors throu...

The utility of artificial intelligence in gastrointestinal oncology: A systematic review of randomized controlled trials.

Cancer
In the field of gastrointestinal oncology, the development of novel artificial intelligence (AI) processes may help with multiple aspects of cancer care delivery. However, a comprehensive understanding of the current utility of AI in gastrointestinal...

Whole Exome Sequencing on FHIR: Towards Adoption in Clinical Practice for Precision Oncology Pipelines.

Studies in health technology and informatics
INTRODUCTION: Whole Exome Sequencing (WES) promises to open a new range of personalized treatments due to breaking the limits of former panel-based methods of molecular analysis. While the methodology is well established and already included in clini...

Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Artificial intelligence models like GPT-4 (OpenAI) have the potential to support clinical decision-making in oncology. This study aimed to assess the consistency between multidisciplinary tumor board (MTB) decisions and GPT-4 model predictio...

Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb.

JMIR cancer
Digital health interventions offer promise for scalable and accessible health care, but access is still limited by some participatory challenges, especially for disadvantaged families facing limited health literacy, language barriers, low income, or ...

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

PLoS computational biology
Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help under...

Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may allow for real-time modifications based on emergi...

Driving Knowledge to Action: Building a Better Future With Artificial Intelligence-Enabled Multidisciplinary Oncology.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and treat cancer. From earlier and more accurate diagnoses to personalized treatment planning, AI's impact i...

The Limitations of Artificial Intelligence in Head and Neck Oncology.

Advances in therapy
Artificial intelligence (AI) is revolutionizing head and neck oncology, offering innovations in tumor detection, treatment planning, and patient management. However, its integration into clinical practice is hindered by several limitations. These inc...

Automating Performance Status Annotation in Oncology Using Llama-3.

Studies in health technology and informatics
This work explores the automated extraction of medical information from Dutch clinical notes using Llama-3 and a limited amount of annotations. We compared zero-, one- and few-shot learning for the extraction of performance status of patients with pa...