Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …

Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis,
which is critical in reducing mortality, is challenging as many of its signs and symptoms are …

The advent of transformers has brought about significant advancements in traditional
machine learning tasks. However, their pervasive deployment has raised concerns about …

Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Bidirectional Encoder Representations from Transformers (BERT) and BERT-based
approaches are the current state-of-the-art in many natural language processing (NLP) …

Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained
Transformer) have made tremendous advances recently and are rapidly transforming the …

The ambition of precision medicine is to design and optimize the pathway for diagnosis,
therapeutic intervention, and prognosis by using large multidimensional biological datasets …

Since the past decade, the interest towards more precise and efficient healthcare techniques
with special emphasis on diagnostic techniques has increased. Artificial Intelligence has …