JMIR AI

Cancer progression is an important outcome in cancer research. However, it is frequently documented only in electronic health records (EHRs) as unstructured text, which requires lengthy and costly chart reviews to extract for retrospective studies.

As physicians spend up to twice as much time on EHR tasks as on direct patient care, digital scribes have emerged as a promising solution to restore patient-clinician communication and reduce documentation burden—making it essential to study their real-world impact on clinical workflows, efficiency, and satisfaction.

Multidisciplinary care management teams must rapidly prioritize interventions for patients with complex medical and social needs. Current approaches rely on individual training, judgment, and experience, missing opportunities to learn from longitudinal trajectories and prevent adverse outcomes through recommender systems.

Early diagnosis of diabetes is essential for early interventions to slow the progression of dysglycaemia and its comorbidities. However, among individuals with diabetes, about 23% were unaware of their condition.

Australians can face significant challenges in navigating the healthcare system, especially in rural and regional areas. Generative search tools, powered by large language models (LLMs), show promise in improving health information retrieval by generating direct answers. However, concerns remain regarding their accuracy and reliability when compared to traditional search engines, in a healthcare context.

Electronic patient records are a valuable yet underused data source; they have been explored in research using natural language processing, but not yet within a third-sector organization.

Free-text clinical data that is unstructured and narrative in nature can provide a rich source of patient information, but extracting research quality clinical phenotypes from these data remains a challenge. Manually reviewing and extracting clinical phenotypes from free-text patient notes is a time-consuming process and not suitable for large scale datasets. On the other hand, automatically extracting clinical phenotypes can be a challenging task due to medical researchers lacking gold-standard annotated references and other purpose-built resources including software. Recent large language models (LLMs) consisting of billions of parameters can understand natural language instructions (prompts) which helps them adapt to different domains and tasks without the need for specific training data. This makes them suitable for clinical applications, though their use in this field is still limited.

Pathophysiological responses to viral infections such as COVID-19 significantly affect sleep duration, sleep quality, and concomitant cardiorespiratory function. The widespread adoption of consumer smart bed technology presents a unique opportunity for unobtrusive, real-world, longitudinal monitoring of sleep and physiological signals, which may be valuable for infectious illness surveillance and early detection. During the COVID-19 pandemic, scalable and noninvasive methods for identifying subtle early symptoms in naturalistic settings became increasingly important. Existing digital health studies have largely relied on wearables or patient self-report, with limited adherence and recall bias. In contrast, smart bed–derived signals enable high-frequency objective data capture with minimal user burden.

Accurate and timely electrocardiogram (ECG) interpretation is critical for diagnosing myocardial infarction (MI) in emergency settings. Recent advances in multimodal Large Language Models (LLMs), such as Chat Generative Pre-trained Transformer (ChatGPT, Gemini), have shown promise in clinical interpretation for medical imaging. However, whether these models analyze waveform patterns or simply rely on text cues remains unclear, underscoring the need for direct comparisons with dedicated ECG artificial intelligence (AI) tools.

Disease modifying therapies ameliorate disease severity of sickle cell disease (SCD), but hematopoietic cell transplantation (HCT) and more recently autologous gene therapy are the only treatments that have curative potential for sickle cell disease (SCD). While registry-based studies provide population-level estimates they do not address the uncertainty regarding individual outcomes of HCT. Computational machine learning (ML) has the potential to identify generalizable predictive patterns and quantify uncertainty in estimates thereby improving clinical decision-making. There is no existing ML Model for SCD and ML models for HCT for other diseases focus on single outcomes rather than all relevant outcomes.

Medical image analysis plays a critical role in brain tumor detection, but training deep learning models often requires large, labeled datasets, which can be time-consuming and costly. This study explores a comparative analysis of machine learning and deep learning models for brain tumor classification, focusing on whether deep learning models are necessary for small medical datasets and whether self-supervised learning can reduce annotation costs.

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.