Journal of Medical Internet Research

Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting AI model development and clinical application.

Heart failure with preserved ejection fraction (HFpEF) is a major clinical manifestation of cardiac amyloidosis (CA), a condition frequently underdiagnosed due to its nonspecific symptomatology. Electronic health records (EHRs) offer a promising avenue for supporting early symptom detection through natural language processing (NLP). However, identifying relevant clinical cues within unstructured narratives, particularly in Spanish, remains a significant challenge due to the scarcity of annotated corpora and domain-specific models.

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The quality and accessibility of menstrual health education in developing nations, including India, remain inadequate due to challenges such as poverty, social stigma, and gender inequality. While community-driven initiatives aim to raise awareness, artificial intelligence (AI) offers a scalable solution for disseminating accurate information. However, existing general-purpose large language models (LLMs) are ill-suited for this task, suffering from low accuracy, cultural insensitivity, and overly complex responses. To address these limitations, we developed MenstLLaMA, a specialized LLM tailored to the Indian context, designed to deliver menstrual health education empathetically, supportively, and accessible.

Mood disorders, including bipolar disorder (BP) and major depressive disorder (MDD), are characterized by significant psychological and behavioral fluctuations, with mobility patterns serving as potential markers of emotional states.

Virtual reality (VR) stationary cycling provides a potential solution to enhance adherence and reduce depressive symptoms, particularly for people with depression. However, high-quality evidence is needed to support its implementation in clinical practice.

Advances in artificial intelligence (AI) promise to reshape the landscape of scientific inquiry. Amidst all these, OpenAI’s latest tool, Deep Research, stands out for its potential to revolutionize how researchers engage with the literature. However, this leap forward presents a paradox - while AI-generated reviews offer speed and accessibility with minimal effort, they raise fundamental concerns about citation integrity, critical appraisal, and the erosion of deep scientific thinking. These concerns are particularly problematic in the context of biomedical research, where evidence quality may influence clinical practice and decision-making. In this piece, we present an empirical evaluation of Deep Research and explore both its remarkable capabilities and inherent limitations. Through structured experimentation, we assess its effectiveness in synthesizing literature, highlight key shortcomings, and reflect on the broader implications of these tools for research training, and the integrity of evidence-based practice. With AI tools increasingly blurring the lines between knowledge generation and critical inquiry, we argue that while AI democratizes access to knowledge, wisdom remains distinctly human.

Hospital Information Systems (HIS) aim to support users in their time-critical routines on hospital wards with accurate and timely information. However, if these systems create blockages to workflows, nurses and physicians develop workarounds to provide care to the patients, nonetheless. Workarounds are both considered negatively, when associated with risks, and positively, when seen as feedback and source of innovation. Learning about the antecedents of workarounds allows for the establishment of control mechanisms, under the promise of enhanced patient safety.

The most common symptom of atopic dermatitis (AD) is pruritus, which is often exacerbated at night and leads to nocturnal scratching and sleep disturbance. The quantification of nocturnal scratching provides an objective measure, which could be used as a clinical trial endpoint tracking this AD-related behavior. However, it is not clear how digital health technologies (DHTs) intended to measure scratching perform in the real-world environment of patient homes.

Perioperative education is crucial for optimizing outcomes in neuroendovascular procedures, where inadequate understanding can heighten patient anxiety and hinder care plan adherence. Current education models, reliant on traditional consultations and printed materials, often lack scalability and personalization. Artificial intelligence (AI)–powered chatbots have demonstrated efficacy in various health care contexts; however, their role in neuroendovascular perioperative support remains underexplored. Given the complexity of neuroendovascular procedures and the need for continuous, tailored patient education, AI chatbots have the potential to offer tailored perioperative guidance to improve patient education in this specialty.

Healthcare systems are increasingly facing challenges posed by the aging of populations. In particular hospitalization, both initial and subsequent, which is often observed among elderly patients. Yet, research suggests that nearly 23% of all hospitalizations could be avoided. In this perspective, remote patient monitoring (RPM) systems are emerging as a promising solution, enabling professionals to detect and manage patient complexities early within home-based care settings.

Clinical problem-solving requires processing of semantic medical knowledge such as illness scripts and numerical medical knowledge of diagnostic tests for evidence-based decision-making. As large language models (LLMs) show promising results in many aspects of language-based clinical practice, their ability to generate non-language evidence-based answers to clinical questions is inherently limited by tokenization.