Information Technology

Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation.

Prostate magnetic resonance imaging (MRI) reporting is a high-impact communication task because small differences in lesion laterality, sector localization, lesion size, Prostate Imaging-Reporting and Data System (PI-RADS) categorization, or staging language can change biopsy targeting, surveillance, counseling, and treatment planning. At the same time, widespread patient-portal access means that ...

Using natural language processing and machine learning to identify social needs in patient medical notes: model training with balanced versus real-world datasets.

OBJECTIVE: To evaluate natural language processing (NLP) and machine learning (ML) approaches for identifying social needs in electronic health record (EHR) notes, using balanced versus real-world imbalanced datasets. MATERIALS AND METHODS: A rule-based NLP framework using definite, non-negated keywords flagged social needs domains and "Any Social Need" in unstructured notes. Logistic regression, ...

Development and evaluation of a multi-source machine learning model to predict critical illness and death in patients with substance misuse.

OBJECTIVES: Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors....

The role of dataset integrity, calibration and signal quality in neural network-based blood pressure estimation.

Cuffless blood pressure (BP) estimation via photoplethysmography (PPG) and machine learning has been widely studied, yet reported accuracy remains bel...

Large language models in spine care and research.

BACKGROUND: Large language models (LLMs) have emerged as powerful transformer-based systems capable of capturing long-range dependencies and complex s...

Clinical Utility of LLM-assisted Chart Review for the Detection of Bleeding Events.

INTRODUCTION: Red blood cell (RBC) transfusions are frequently administered in the intensive care unit (ICU) and are independently associated with inc...

Exploring Perceptions of Leveraging AI to Improve Outcomes in Maternal, Sexual, and Reproductive Health in Sub-Saharan Africa: Exploratory Qualitative Study.

BACKGROUND: AI has the potential to transform health care in low- and middle-income countries, where access to quality care remains limited. Maternal,...

Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States.

OBJECTIVE: To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large languag...

Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.

BACKGROUND AND OBJECTIVES: Machine learning (ML) and natural language processing (NLP) approaches are increasingly used to support nuanced phenotyping...

Development and validation of machine learning models to predict risk of undiagnosed dementia using healthcare claims and electronic health record data.

BackgroundApproximately half of people living with Alzheimer's disease and related dementias are undiagnosed.ObjectiveTo develop and validate algorith...

Rapid Resolution of Hypoxemia is Common and Associated with Lower Mortality Among Intubated Patients with Acute Hypoxemic Respiratory Failure.

ObjectiveTo evaluate the prevalence, clinical trajectory, and biologic phenotype of patients with rapidly improving acute hypoxemic respiratory failur...

Common data element (CDE) and AI-Enabled approaches for clinical data Management: Workshop proceedings from the NIH INCLUDE Project.

OBJECTIVE: To evaluate challenges in managing multisite, longitudinal, and multisystem clinical data for Down syndrome (DS) research and identify appr...

Prediction of Antimicrobial Resistance in People Living With Cystic Fibrosis Using Machine Learning.

Antimicrobial resistance (AMR) is a growing challenge in people living with cystic fibrosis (pwCF), who often experience chronic lung infection and re...

Plastic Surgery Search Engine Optimization in the Artificial Intelligence Era.

Marketing in aesthetic plastic surgery has shifted from traditional print and television toward digital-first strategies, with social media and search...

Unsupervised Global CBC/RUO/CPD Phenotyping Identifies Haematological Clusters Enriched for Thrombocytopenia Severity and Mechanisms.

INTRODUCTION: Thrombocytopenia has heterogeneous mechanisms, including peripheral destruction, consumption, and reduced marrow production. This study ...

Perspective: The Future of Peritoneal Dialysis in the Era of Telemedicine and Artificial Intelligence.

Peritoneal dialysis (PD) represents one of the major modalities for home-based renal replacement therapy, offering autonomy and flexibility to patient...

Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.

BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS) is a multisystemic hereditary connective tissue disorder characterized by generalized joint hype...

Comparative effectiveness of ambient documentation tools in primary care tool-specific variations in efficiency, documentation burden, and productivity over time.

OBJECTIVES: To evaluate the comparative effectiveness of ambient documentation tools (ADTs) with distinct architectures (Tablet-Based Virtual Human-As...

Building a Natural Language Processing Augmented Information Support System to Enhance Supportive Care for Patients With Prostate Cancer and Families: User-Centered, Iterative Approach.

BACKGROUND: Patients with prostate cancer and their families face significant challenges during transitions from diagnosis to treatment and posttreatm...

Automated Features, Algorithms, and Technologies of Electronic Early Warning/Track-and-Trigger Systems: Systematic Review.

BACKGROUND: Electronic early warning/track-and-trigger systems (EW/TTS) are crucial for patient monitoring, detecting clinical deterioration (CD), and...