An overview of artificial intelligence in oncology
E Farina, JJ Nabhen, MI Dacoregio, F Batalini… - Future science …, 2022 - Taylor & Francis
Cancer is associated with significant morbimortality globally. Advances in screening,
diagnosis, management and survivorship were substantial in the last decades, however …
diagnosis, management and survivorship were substantial in the last decades, however …
Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine
ZH Chen, L Lin, CF Wu, CF Li, RH Xu… - Cancer …, 2021 - Wiley Online Library
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution
of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is …
of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is …
Cohortgpt: An enhanced gpt for participant recruitment in clinical study
Participant recruitment based on unstructured medical texts such as clinical notes and
radiology reports has been a challenging yet important task for the cohort establishment in …
radiology reports has been a challenging yet important task for the cohort establishment in …
Clinical natural language processing for radiation oncology: a review and practical primer
Natural language processing (NLP), which aims to convert human language into
expressions that can be analyzed by computers, is one of the most rapidly developing and …
expressions that can be analyzed by computers, is one of the most rapidly developing and …
[HTML][HTML] Large language models for healthcare data augmentation: An example on patient-trial matching
The process of matching patients with suitable clinical trials is essential for advancing
medical research and providing optimal care. However, current approaches face challenges …
medical research and providing optimal care. However, current approaches face challenges …
Multi-disciplinary fairness considerations in machine learning for clinical trials
While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …
tremendously in recent years, a number of barriers prevent deployment in medical practice …
Llm for patient-trial matching: Privacy-aware data augmentation towards better performance and generalizability
The process of matching patients with suitable clinical trials is essential for advancing
medical research and providing optimal care. However, current approaches face challenges …
medical research and providing optimal care. However, current approaches face challenges …
Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis
Background The aim of this study is to provide a comprehensive understanding of the
current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its …
current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its …
Machine learning approaches for electronic health records phenotyping: a methodical review
S Yang, P Varghese, E Stephenson… - Journal of the …, 2023 - academic.oup.com
Objective Accurate and rapid phenotyping is a prerequisite to leveraging electronic health
records for biomedical research. While early phenotyping relied on rule-based algorithms …
records for biomedical research. While early phenotyping relied on rule-based algorithms …
A systematic review on natural language processing systems for eligibility prescreening in clinical research
Objective We conducted a systematic review to assess the effect of natural language
processing (NLP) systems in improving the accuracy and efficiency of eligibility …
processing (NLP) systems in improving the accuracy and efficiency of eligibility …