AIMC Topic: Retrospective Studies

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Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.

Journal of medical Internet research
BACKGROUND: While machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, US health care providers and hospital administrators have increasingly embraced ML, particularly through its integration...

Quantifying Emergency Medicine Residency Learning Curves Using Natural Language Processing: Retrospective Cohort Study.

JMIR medical education
BACKGROUND: The optimal duration of emergency medicine (EM) residency training remains a subject of national debate, with the Accreditation Council for Graduate Medical Education considering standardizing all programs to 4 years. However, empirical d...

High Concordance Between GPT-4o and Multidisciplinary Tumor Board Decisions in Breast Cancer: A Retrospective Decision Support Analysis.

Journal of medical systems
Large language models (LLMs) such as ChatGPT have gained attention for their potential to assist clinical decision-making in oncology. However, real-world validation of these models against multidisciplinary tumor board (MTB) recommendations-particul...

Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

Clinical and experimental medicine
Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV da...

When used for veterinary triage, artificial intelligence models recognise emergencies but are more likely than veterinary staff to flag non-urgent cases as urgent.

The Veterinary record
BACKGROUND: This study assesses the capability of ChatGPT and nurses in accurately triaging emergency patients compared to veterinarians. METHODS: Retrospective observational study of canine patients that presented at a private veterinary specialist ...

Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study.

JMIR medical informatics
BACKGROUND: Falls among hospitalized patients are a critical issue that often leads to prolonged hospital stays and increased health care costs. Traditional fall risk assessments typically rely on standardized scoring systems; however, these may fail...

Artificial inteligence reading of cystometric traces provides good correlation with human diagnosis.

World journal of urology
AIM: Urodynamic studies are essential for diagnosing lower urinary tract dysfunction but are expert-dependent and time-consuming. Artificial intelligence (AI), notably machine learning (ML) and deep learning (DL) may help automate and standardize int...

Assessing the Accuracy of Artificial Intelligence in Detecting Intracranial Aneurysms in a Clinical Setting Relative to Neuroradiologists.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Intracranial aneurysms (IAs), detected in 2%-5% of the population, represent a major health care issue because ruptured aneurysms with resultant hemorrhage are associated with severe morbidity or mortality. With the increasing...

Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis.

PloS one
INTRODUCTION: Prognostication at 90 and 180 days after thrombolysis for acute ischemic stroke (AIS) is critical, yet the temporal evolution of key predictors remains inadequately understood. The utility of machine learning for systematically comparin...