JMIR AI

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.

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.