Integrating Artificial Intelligence (AI) through Natural Language Processing (NLP) can improve veterinary medical oncology clinical record analytics. Named Entity Recognition (NER), a critical component of NLP, can facilitate efficient data extractio...
Artificial intelligence (AI) is addressing many expectations for healthcare practitioners and patients in oncology. It has the potential to deeply transform medical practices as we know them today: improving early diagnosis by analysing large quantit...
Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electro...
INTRODUCTION: No consensus exists on performance standards for evaluation of generative artificial intelligence (AI) to generate medical responses. The purpose of this study was the assessment of Chat Generative Pre-trained Transformer (ChatGPT) to a...
PURPOSE: To critically examine the current state of machine learning (ML) models including patient-reported outcome measure (PROM) scores in cancer research, by investigating the reporting quality of currently available studies and proposing areas of...
This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) techniques to address challenges in patient recruitment for improved efficiency in drug development....
Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intell...
Journal of cancer education : the official journal of the American Association for Cancer Education
Sep 30, 2024
This study evaluated the perspectives and educational needs of Canadian oncology residents with regard to artificial intelligence (AI) in medicine, exploring the influence of factors such as program of choice, gender, and tech literacy on their attit...
OBJECTIVE: This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria.
While AI holds immense potential for accelerating advances in oncology, we must be intentional in developing and applying these technologies responsibly, equitably, and ethically. One path forward is for cancer care providers and researchers to be am...
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