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JMIR AI
Scopus
ISSN 2817-1705 · JMIR Publications · 210 artikel di indeks
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Artikel Jurnal
The Role of AI in Improving Digital Wellness Among Older Adults: Comparative Bibliometric Analysis
Abstract BackgroundAdvances in artificial intelligence (AI) have revolutionized digital wellness by providing innovative solutions for health, social connectivity, and overall well-being. Despite these advancements, the older population often struggles with barriers such as accessibility, digital…
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Artikel Jurnal
Treatment Recommendations for Clinical Deterioration on the Wards: Development and Validation of Machine Learning Models
Abstract BackgroundClinical deterioration in general ward patients is associated with increased morbidity and mortality. Early and appropriate treatments can improve outcomes for such patients. While machine learning (ML) tools have proven successful in the early identification of clinical…
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Artikel Jurnal
Facial Expression–Based Evaluation of the Emotion Estimation Software Kokoro Sensor in Healthy Individuals: Validation and Reliability Pilot Study
Abstract BackgroundIn recent years, artificial intelligence (AI) systems have increasingly been used to assess emotional states in health care. AI offers a safe, quick, user-friendly, and objective emotional evaluation method. However, evidence supporting its implementation in health care remains…
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Performance of Large Language Models Under Input Variability in Health Care Applications: Dataset Development and Experimental Evaluation
Abstract BackgroundLarge language models (LLMs) are increasingly integrated into health care, where they contribute to patient care, administrative efficiency, and clinical decision-making. Despite their growing role, the ability of LLMs to handle imperfect inputs remains underexplored. These…
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Artikel Jurnal
Ambient AI Documentation and Patient Satisfaction in Outpatient Care: Retrospective Pilot Study
Abstract BackgroundPatient experience is a critical consideration for any health care institution. Leveraging artificial intelligence (AI) to improve health care delivery has rapidly become an institutional priority across the United States. Ambient AI documentation systems such as Dragon Ambient…
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Artikel Jurnal
Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting
AbstractArtificial intelligence (AI) is increasingly used to support medical interpreting and public health communication, yet current systems introduce serious risks to accuracy, confidentiality, and equity, particularly for speakers of low-resource languages. Automatic translation models often…
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Artikel Jurnal
Exploring Clinician Perspectives on Artificial Intelligence in Primary Care: Qualitative Systematic Review and Meta-Synthesis
Abstract BackgroundRecent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinical tasks, but concerns regarding biases, lack of regulation, and potential technical issues pose significant challenges. The lack of a clear…
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Application of AI Models for Preventing Surgical Complications: Scoping Review of Clinical Readiness and Barriers to Implementation
Abstract BackgroundThe impact of surgical complications is substantial and multifaceted, affecting patients and their families, surgeons, and health care systems. Despite the remarkable progress in artificial intelligence (AI), there remains a notable gap in the prospective implementation of AI…
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Artikel Jurnal
Assessment of the Modified Rankin Scale in Electronic Health Records With a Fine-Tuned Large Language Model: Development and Internal Validation
Abstract BackgroundThe modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studies. The mRS can be assessed retrospectively from electronic health records (EHRs), but this process is labor-intensive and prone to…
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Artikel Jurnal
AI-Generated Images of Substance Use and Recovery: Mixed Methods Case Study
Abstract BackgroundImages created with generative artificial intelligence (AI) tools are increasingly used for health communication due to their ease of use, speed, accessibility, and low cost. However, AI-generated images may bring practical and ethical risks to health practitioners and the…
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Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint Analysis
Abstract BackgroundSmart home technology powered by AI can detect anomalies and make emergency calls, enabling residents to live safely and independently. However, the adoption of such technologies for medical emergency detection remains limited. ObjectiveThis study aimed to explore consumer…
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Artikel Jurnal
Interpretable Multihorizon Glucose Forecasting for Assessing Nutritional Information Impact in Type 1 Diabetes Management: Model Development and Validation Study
Abstract BackgroundType 1 diabetes is characterized by absolute insulin deficiency, requiring exogenous insulin therapy to maintain blood glucose levels within safe ranges. Postprandial glucose control remains particularly challenging, and current meal-related strategies are mainly based on…
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Artikel Jurnal
Implicit Bias in Large Language Model Diagnosis of Eating Disorders: Experimental Vignette Study
Abstract BackgroundLarge language models (LLMs) are increasingly deployed in mental health applications, yet growing evidence suggests they encode algorithmic biases that influence clinical outputs. Because these models now mediate patient-facing decisions, such biases carry the potential for…
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Artikel Jurnal
The Perceived Influence of AI on the Craftsmanship of Physicians: Qualitative Interview and Focus Group Study
Abstract BackgroundHealth care systems face rising demand and persistent staff shortages, intensifying pressure on the quality and sustainability of care. Artificial intelligence (AI) is increasingly being introduced to improve efficiency and decision support across clinical domains. While these…
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AI-Based Approaches for the Identification and Quantification of Traumatic Brain Injury in Computed Tomography Imaging: Systematic Review
Abstract BackgroundTraumatic brain injury (TBI) is a leading cause of global disability and mortality, requiring timely diagnosis to prevent secondary injury. Manual computed tomographic (CT) evaluation often causes diagnostic delays, especially in smaller hospitals with limited radiological…
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Artikel Jurnal
Enhancing Patients’ Informed Consent Through AI: Systematic Review
Abstract BackgroundInformed consent is a cornerstone of medical ethics, ensuring that patients understand the risks, benefits, and alternatives of procedures before making health care decisions. However, challenges such as complex medical language, time constraints, and variations in patient…
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Artikel Jurnal
Estimating a Physiological Lung Function Score and Biological Sex Using Pulmonary Function Tests and Machine Learning: Retrospective Study
Abstract BackgroundSex and age have long been known to affect lung function. Several biological variables and anatomical factors may contribute to sex- and age-related differences in pulmonary metrics. ObjectiveWe hypothesized that a machine learning model could be trained to predict a person’s…
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Artikel Jurnal
Multimodal GPT-5 for Predicting Poor Functional Outcomes After Intracerebral Hemorrhage in the Emergency Department: Validation Study
Abstract BackgroundIn the emergency department, rapid prognostic assessment of patients with intracerebral hemorrhage (ICH) is essential for guiding early management decisions, particularly when stroke specialists are not immediately available. Recent advances in large language models have…
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Artikel Jurnal
Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine
AbstractLarge language models (LLMs) are being increasingly incorporated into clinical workflows due to their ability to synthesize medical knowledge and support diagnosis and treatment planning. However, their opaque internal decision-making processes limit trust, reliability, and safe clinical…
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Artikel Jurnal
Ethics and Fairness Considerations in AI-Based Deception Detection Technologies for Mental Health Applications: Focus Group Study
Abstract BackgroundArtificial intelligence (AI) technologies are increasingly being integrated into mental health settings to support tasks such as clinical documentation and decision-making. In parallel, AI-enabled deception detection, which leverages multimodal behavioral cues like facial…