Generative AI in medicine and healthcare: promises, opportunities and challenges
P Zhang, MN Kamel Boulos - Future Internet, 2023 - mdpi.com
Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI's
ChatGPT, that can be prompted to generate various types of content. In this narrative review …
ChatGPT, that can be prompted to generate various types of content. In this narrative review …
A review on bayesian deep learning in healthcare: Applications and challenges
In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence,
and it has been deployed in different fields of healthcare applications such as image …
and it has been deployed in different fields of healthcare applications such as image …
Machine learning techniques for biomedical natural language processing: a comprehensive review
EH Houssein, RE Mohamed, AA Ali - IEEE Access, 2021 - ieeexplore.ieee.org
The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …
large amount of real-world data, leading to new areas for clinical research. Natural language …
Adverse drug event detection using natural language processing: A sco** review of supervised learning methods
To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …
Artificial intelligence for quantitative modeling in drug discovery and development: An innovation and quality consortium perspective on use cases and best practices
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered
in a new era of possibilities across various scientific domains. One area where these …
in a new era of possibilities across various scientific domains. One area where these …
Transforming epilepsy research: A systematic review on natural language processing applications
Despite improved ancillary investigations in epilepsy care, patients' narratives remain
indispensable for diagnosing and treatment monitoring. This wealth of information is …
indispensable for diagnosing and treatment monitoring. This wealth of information is …
Distilling large language models for biomedical knowledge extraction: A case study on adverse drug events
Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities
across a wide range of tasks, including health applications. In this paper, we study how …
across a wide range of tasks, including health applications. In this paper, we study how …
Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets
Objective The primary objective of this review is to investigate the effectiveness of machine
learning and deep learning methodologies in the context of extracting adverse drug events …
learning and deep learning methodologies in the context of extracting adverse drug events …
Sentence-level aspect-based sentiment analysis for classifying adverse drug reactions (ADRs) using hybrid ontology-XLNet transfer learning
This paper presents a hybrid ontology-XLNet sentiment analysis classification approach for
sentence-level aspects. The main objective of the proposed approach allows discovering …
sentence-level aspects. The main objective of the proposed approach allows discovering …
[HTML][HTML] Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework
Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has
been learned about the novel coronavirus since its emergence, there are many open …
been learned about the novel coronavirus since its emergence, there are many open …