Medicolegal

Evaluating an Ambient Artificial Intelligence Scribe for Documentation Quality and Efficiency in Psychiatric Consultations: A Simulation-based Study

Documentation demands in psychiatric practice diminish time for direct patient care and are associated with clinician burnout. Ambient artificial intelligence (AI) scribes may facilitate more efficient and higher-quality documentation while reducing clinician workload and preserving the integrity of the clinical encounter. This study aims to determine the impact of an ambient AI scribe in improvin...

Clinical evaluation of a natural language processing system for assisting structured diagnosis recording at the point of care: MiADE (Medical Information AI Data Extractor)

Structured recording of key information such as diagnoses is essential for safe, efficient patient care, but is currently done incompletely because it is time consuming for clinicians. We developed a natural language processing system called MiADE integrated with the Epic electronic health record to provide suggestions for structured diagnosis entries at the point of care. To evaluate the usabilit...

DualAlign: Generating Clinically Grounded Synthetic Data

Synthetic clinical data are increasingly important for advancing AI in healthcare, given strict privacy constraints on real-world EHRs, limited availa...

Assessment of a zero-shot large language model in measuring documented goals-of-care discussions

Goals-of-care (GOC) discussions and their documentation are important process measures in palliative care. However, existing natural language processi...

Compartment-specific Fat Distribution Profiles have Distinct Relationships with Cardiovascular Ageing and Future Cardiovascular Events

Obesity is a global public health priority and a major risk factor for cardiovascular disease (CVD). Emerging evidence indicates variation in patholog...

Accents Still Confuse AI: Systematic Errors in Speech Transcription and LLM-Based Remedies

Accurate and timely documentation in the electronic health record (EHR) is essential for delivering safe and effective patient care. AI-enabled medica...

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model

Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across str...

Retrospective Evaluation of a Generative AI-Enabled Electronic Medical Record System in Primary Health Care Facilities in Kenya

We conducted a retrospective evaluation of an electronic medical record-embedded large language model (LLM) clinical decision support system deployed ...

Leveraging Foundation Models in Maternal and Child Health: A Systematic Review

Maternal and child health (MCH) represents a critical domain requiring accurate, timely, and data-driven decision-making to optimize outcomes from pre...

A Scoping Review of Algorithmic Equity, Data Diversity, and Inclusive Design in the Transformer Era of Clinical NLP

The rapid digitization of healthcare has positioned transformer-based natural language processing (NLP) models as powerful tools for managing clinical...

Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge

Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize re...

A medical algorithmic audit framework for evaluating the safety, equity, and quality of an AI Scribe tool in a paediatric developmental assessment clinic

Any tool that can reduce the administrative burden on healthcare providers while preserving safe, accountable and high-quality medical documentation i...

Evaluating an LLM-Assisted Workflow for Clinical Documentation: A Pilot Randomized Controlled Trial on Time and Quality

Large language models (LLMs) have been investigated for clinical documentation, with concerns about hallucinations and factual errors. Clinician revie...

Falls in Assisted Living Facilities: Can AI improve documentation and reduce injury?

Falls among elderly residents in assisted living facilities (ALFs) are prevalent, costly, and frequently under-documented. AUGi, a wall-mounted device...

Predicting Amyloid Positivity Through Proteomic and Machine Learning Approaches

Alzheimer’s disease is a progressive neurodegenerative disorder where early detection remains difficult. To address this challenge, we analysed a larg...

Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults

Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)–assisted scr...

Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology Reports

Surgical pathology reports provide essential diagnostic information critical for cancer staging, treatment planning, and cancer registry documentation...

How effective is generative AI advice for the academic advancement of faculty?

The SingHealth Duke-NUS Academic Medical Center manages over 2,800 clinical faculty members and processes over 400 appointments and promotions annuall...

Ambient Only vs. Longitudinal Data-Enhanced AI Documentation: A Pilot Study Quantifying the Value of Historical Clinical Context in Primary Care

Ambient artificial intelligence (AI) clinical documentation tools have gained rapid adoption in healthcare to address physician burnout from documenta...

Agent-Based Large Language Model System for Extracting Structured Data from Breast Cancer Synoptic Reports: A Dual-Validation Study

To develop and validate an agent-based Large Language Model (LLM) system for extracting structured data from breast cancer synoptic pathology reports ...