JMIR AI

Systematic literature reviews are foundational for synthesizing evidence across diverse fields, with particular importance in guiding research and practice in health and biomedical sciences. However, they are labor-intensive due to manual data extraction from multiple studies. As large language models (LLMs) gain attention for their potential to automate research tasks, understanding their ability to accurately extract information from academic papers is critical for advancing systematic reviews.

Artificial Intelligence (AI) has the potential to transform global healthcare, with extensive application in Brazil, particularly for diagnosis (D) and screening (S).

Artificial Intelligence (AI) is becoming increasingly popular in the scientific field, as it allows for the analysis of extensive datasets, summarize results, and assist in writing academic papers.

Clinical notes house rich, yet unstructured, patient data, making analysis challenging due to medical jargon, abbreviations, and synonyms causing ambiguity. This complicates real-time extraction for decision support tools.

Tacrolimus is the backbone of immunosuppression in solid organ transplantation, requiring precise dosing due to its narrow therapeutic range. Maintaining therapeutic tacrolimus levels post-operatively is challenging due to diverse patient characteristics, donor organ factors, drug interactions, and evolving perioperative physiology.

Rare diseases, which affect millions of people worldwide, pose a major challenge for diagnosis, as it often takes years before an accurate diagnosis can be made. This delay results in substantial burdens for patients and healthcare systems, as misdiagnoses lead to inadequate treatment and increased costs. AI-powered symptom checkers (SCs) present an opportunity to flag rare diseases earlier in the diagnostic work-up. However, these tools are primarily based on published literature, which often contains incomplete data on rare diseases, resulting in compromised diagnostic accuracy. Integrating expert interview insights into SC models may enhance their performance, ensuring rare diseases are considered sooner and diagnosed more accurately.

Limited research exists evaluating AI performance on standardized pediatric assessments. This study evaluated three leading AI models on pediatric board preparation questions.

Since the release of ChatGPT and other large language models (LLMs), there has been a significant increase in academic publications exploring their capabilities and implications across various fields, such as Medicine, Education, and Technology.

Delirium is a prevalent condition in intensive care units (ICUs), often leading to adverse outcomes. Hypoactive delirium is particularly difficult to detect. Despite the progress made in research and the development of new tools, timely identification of hypoactive delirium remains clinically challenging to detect due to its dynamic nature, lack of human resources, lack of reliable monitoring tools, and subtle clinical signs that include hypovigilance. Machine learning detection models could support the identification of hypoactive delirium episodes by better detecting episodes of hypovigilance.

Intensive care units (ICUs) treat patients with life-threatening illnesses. Worldwide, intensive care demand is massive. Predicting patient outcomes in ICUs holds significant importance for health care operation management. Nevertheless, it remains a challenging problem that researchers and health care practitioners have yet to overcome. While the newly emerging health digital trace data offer new possibilities, such data contain complex time series and patterns. Although researchers have devised severity score systems, traditional machine learning models with feature engineering, and deep learning models that use raw clinical data to predict ICU outcomes, existing methods have limitations.

Accurately assigning International Classification of Diseases, 10th Revision (ICD-10) codes is critical for clinical documentation, reimbursement processes, epidemiological studies, and healthcare planning. Manual coding is time-consuming, labor-intensive, and prone to errors, underscoring the need for automated solutions within the Norwegian healthcare system. Recent advances in natural language processing (NLP) and transformer-based language models have shown promising results in automating ICD coding in several languages. However, prior work has focused primarily on English and other high-resource languages, leaving a gap in Norwegian-specific clinical NLP research.

Recent progress has demonstrated the potential of deep learning models in analyzing ECG pathologies. However, this method is intricate, expensive to develop, and designed for specific purposes. Large language models show promise in medical image interpretation, yet their effectiveness in ECG analysis remains understudied. GPT-4o, a multimodal AI model, capable of processing images and text without task-specific training, may offer an accessible alternative.