JMIR Medical Informatics

Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain–specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.

Residents of elderly facilities are vulnerable to COVID-19 outbreaks. Nevertheless, timely recognition of outbreaks at elderly facilities of public health centers has been impossible in Japan since May 8, 2023, when the Japanese government discontinued aggressive countermeasures against COVID-19 because of the waning severity of the dominant Omicron mutated strain. The Facility for Elderly Surveillance System (FESSy) has been developed to improve information collection.

AI chatbots, particularly in the field of ultrasound medicine, are increasingly used for medical inquiries. However, their performance varies and is influenced by factors such as language, question type, and topic.

The field of digital health solutions (DHS) has grown tremendously over the past years. DHS include tools for self-management, which support individuals to take charge of their own health. Pivotal to adoption is the usability of DHS, as experienced by patients. However, well-known questionnaires that evaluate usability and satisfaction use complex terminology derived from human computer interaction and are therefore not well suited to assess experienced usability of patients using DHS in a home setting.

Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate. We developed a fully automated pipeline based on the Key–bidirectional encoder representations from transformers (BERT) approach and large-scale medical records for continued pretraining, which effectively converts long free text into standard ICD codes. By adjusting parameter settings, such as mixed templates and soft verbalizers, the model can adapt flexibly to different requirements, enabling task-specific prompt learning.

Mental health chatbots have emerged as a promising tool for providing accessible and convenient support to individuals in need. Building on our previous research on digital interventions for loneliness and depression among Korean college students, this study addresses the limitations identified and explores more advanced artificial intelligence–driven solutions.

In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators.

Heart failure patients frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and healthcare systems. Accurate predictive tools are crucial for guiding clinical decision-making and optimizing patient care. However, the effectiveness of existing models tailored specifically to the Chinese heart failure population is still limited.

Chronic pain is widespread and carries a heavy disease burden, and there is a lack of effective outpatient pain management. As an emerging internet medical platform in China in recent years, internet hospitals have been successfully applied to the management of chronic diseases. There are also a certain number of chronic pain patients using internet hospitals for pain management. However, no studies have investigated the effectiveness of pain management via internet hospitals.

Telehealth programs and wearable sensors that enable patients to monitor their vital signs have expanded due to the COVID-19 pandemic. The electronic national early warning score (E-NEWS) system helps identify and respond to acute illness.

Health and clinical activity data are a vital resource for research, improving patient care and service efficiency. Healthcare data is inherently complex, and its acquisition, storage, retrieval, and subsequent analysis require a thorough understanding of the clinical pathways underpinning such data. Better use of healthcare data could lead to improvements in patient care and service delivery. However, this depends on the identification of relevant datasets.

Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging. However, existing LLMs encounter challenges in rapidly adapting to specialized guideline updates. In this study, we fine-tuned an LLM specifically for lung cancer pathologic staging, enabling it to incorporate the latest guidelines for pathologic TN classification.