Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

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 …

Recent advancements in large language models (LLMs) have led to the development of
highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional …

Electronic health records (EHRs) are widely used in epidemiological research, but the
validity of the results is dependent upon the assumptions made about the healthcare system …

This paper investigated the use of language models and deep learning techniques for
automating disease prediction from symptoms. Specifically, we explored the use of two …

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery.
Interestingly, many examples of autonomous actions already exist and have been around for …

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 …

Objective In this systematic review, we aim to synthesize the literature on the use of natural
language processing (NLP) and text mining as they apply to symptom extraction and …

Background The recent, rapid development of digital technologies offers new possibilities for
more efficient implementation of electronic health record (EHR) and personal health record …

Objectives Unstructured free-text patient feedback contains rich information, and analysing
these data manually would require a lot of personnel resources which are not available in …