JMIR Medical Informatics

Machine learning is increasingly used to develop prognostic prediction models for spinal cord injury. Nevertheless, current studies exhibit heterogeneity in outcome measures, predictors, modeling strategies, and validation methods. Moreover, the reporting quality, risk of bias, and clinical applicability of these models have not been systematically evaluated using assessment tools specific to prediction models.

Patient-generated health data (PGHD) can enhance patient-centered care by improving disease awareness and preparedness for clinical encounters. However, automated incorporation of PGHD into electronic medical records (EMRs), which is a prerequisite for broader clinical implementation, remains technically and administratively challenging.

Smart bioelectronics are electronic medical devices that combine hardware and artificial intelligence (AI)–based software. These convergent medical devices analyze bio-signals measured through hardware using AI algorithms and deliver physical stimulation to enhance therapeutic effects.

During January 2024, the US Virgin Islands (USVI) Department of Health (VIDOH) identified a critical need to maintain the cloud-hosted National Electronic Disease Surveillance System Base System (NBS) instance and support the local data modernization initiative. After consulting with federal partners and subject matter experts, VIDOH’s leadership chose to migrate the integrated disease surveillance system to a new platform hosted on Amazon Web Services (AWS) and update the NBS instance to the most advanced version, NBS 7.

Health care providers spend an excessive amount of time within electronic medical record (EMR) systems documenting patient encounters, often amounting to hours of work outside of regular office hours. This affects physician productivity and directly contributes to burnout. Artificial intelligence (AI) is becoming more integrated into medical care, including the development of speech recognition and note generation algorithms. Limited studies exist on how these AI tools affect provider satisfaction, work-life balance, and patient satisfaction.

Lymph node metastasis (LNM) is a critical clinical indicator for determining the initial treatment strategy for patients with lung cancer. However, accurately diagnosing LNM preoperatively remains a significant challenge. Data-driven predictive modeling has become a mainstream approach to address this issue, yet it often overlooks existing clinical knowledge. Large language models (LLMs) have demonstrated the potential to predict clinical risks in a zero-shot manner based on the extensive clinical knowledge learned from large-scale corpora.

The secondary use of health data holds substantial potential for advancing biomedical research, strengthening population health analytics, and enabling artificial intelligence–driven decision-making support. Yet, ensuring that such reuse respects patient autonomy, privacy, and regulatory obligations remains a major challenge. Conventional consent mechanisms are typically static, difficult to revoke, and offer limited transparency or accountability after data disclosure.

This electronic health record–based study highlights distinct diagnostic patterns preceding a diagnosis of light chain (AL) and wild-type transthyretin (ATTRwt) amyloidosis; AL amyloidosis was more frequently preceded by renal, gastrointestinal, neurologic, and clonal plasma cell disorders, whereas ATTRwt amyloidosis was more commonly associated with cardiac manifestations and carpal tunnel syndrome.

Many studies have evaluated the use of wearable monitoring systems to improve patient safety in hospital. Although some have demonstrated effects on intensive care admissions, there remains little evidence of impact on patient outcomes such as mortality, hospital length of stay, and time to antibiotic administration. Very few studies have focused on how wearable monitoring systems are used in clinical practice, including how the rate of manual vital sign measurements (MVSMs) is affected.

Real-time prediction of sepsis is a critical yet highly challenging task. Existing studies face 2 major limitations. First, they often rely on laboratory test results that are not readily available in real time, making timely diagnosis difficult. Second, the patient’s condition evolves as a typical time series, but current methods often adopt coarse modeling strategies, with model architectures that are inefficient to train and deploy effectively.

The differentiation of primary ischemic from secondary nonischemic T-wave inversion (TWI) on electrocardiograms (ECGs) presents a critical and pervasive diagnostic challenge in emergency cardiology. Historical clinical literature reports that clinician-led visual interpretation of isolated TWI yields a positive predictive value of only approximately 50% due to profound morphological ambiguity. This high degree of uncertainty frequently leads to high false-positive rates, resulting in unnecessary, costly, and potentially risky invasive angiographic procedures for patients. Furthermore, although existing deep learning models have attempted to address this clinical bottleneck, they are frequently limited to single-modality, “black box” architectures. Their inability to process complex multimodal data or provide transparent reasoning traces fundamentally limits clinical trust and real-world adoption.

Artificial intelligence has gained relevance due to its potential to reduce the workload in evidence synthesis or bibliometric projects. While the main focus has been lately on the use of instruction-tuned large language models, zero-shot classification models have not been tested for such task. These models are large language models trained on large datasets of labeled data able to categorize text among proposed labels, irrespective of the text domain or the topic. They are relatively small, able to run on consumer-grade computers, and almost hyperparameter-free.