JMIR Medical Informatics

The capabilities of large language models (LLMs) to self-assess their own confidence in answering questions in the biomedical realm remain underexplored.

Research on chronic multimorbidity has increasingly become a focal point with the aging of the population. Many studies in this area require detailed patient characteristic information. However, the current methods for extracting such information are complex, time-consuming, and prone to errors. The challenge of quickly and accurately extracting patient characteristics has become a common issue in the study of chronic disease comorbidities.

In medical practice, improving clinical reasoning and reducing diagnostic errors are essential. OpenAI introduced "OpenAI-o1" with enhanced capabilities for complex reasoning; however, it remains uncertain whether OpenAI-o1 can decrease diagnostic errors compared to the current model, GPT-4.

For the public health community, monitoring recently published articles is crucial for staying informed about the latest research developments. However, identifying publications about studies with specific research designs from the extensive body of public health publications is a challenge with currently available methods.

Medication errors represent a widespread, hazardous, and costly challenge in health care settings. The lack of interoperable medication data within and across hospitals not only creates an administrative burden through redundant data entry but also increases the risk of errors due to human mistakes, imprecise data transformations, and misinterpretations. While digital solutions exist, fragmented systems and nonstandardized data hinder effective medication management.

Stroke patients have high rates of all-cause readmission and case fatality. Limited information is available on how to predict these outcomes.

Teledermatological consultations offer the advantage of rapid diagnosis and care. Since 2019, our institute at the University Medical Center Hamburg-Eppendorf is part of an interdisciplinary team for teledermatological support in German prisons as an alternative to extramural transports of patients.

With the rapid development of artificial intelligence (AI) technology, especially generative AI, large language models (LLMs) have shown great potential in the medical field. Through massive medical data training, it can understand complex medical texts and can quickly analyze medical records and provide health counseling and diagnostic advice directly, especially in rare diseases. However, no study has yet compared and extensively discussed the diagnostic performance of LLMs with that of physicians.

With the improvement of drug evaluation system in China, an increasing number of clinical trials have been launched in Chinese hospitals. However the traditional clinical trial quality management models largely rely on human monitoring and counting, which can be time consuming and is likely to generate errors and biases. There is an urgent need to upgrade and improve the efficiency and accuracy of clinical trial quality monitoring system in hospital based research institutions in China.

The volume of digital data in health care is continually growing. In addition to its use in health care, the health data collected can also serve secondary purposes, such as research. In this context, clinical data warehouses (CDWs) provide the infrastructure and organization necessary to enhance the secondary use of health data. Various data models have been proposed for structuring data in a CDW, including the Informatics for Integrating Biology & the Bedside (i2b2) model, which relies on a relational database. However, this persistence approach can lead to performance issues when executing queries on massive data sets.