AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Diagnosis, Differential

Showing 131 to 140 of 691 articles

Clear Filters

Preoperative Contrast-Enhanced CT-Based Deep Learning Radiomics Model for Distinguishing Retroperitoneal Lipomas and Well‑Differentiated Liposarcomas.

Academic radiology
RATIONALE AND OBJECTIVES: To assess the efficacy of a preoperative contrast-enhanced CT (CECT)-based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroper...

A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.

International journal of computer assisted radiology and surgery
PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D ...

Identifying symptom etiologies using syntactic patterns and large language models.

Scientific reports
Differential diagnosis is a crucial aspect of medical practice, as it guides clinicians to accurate diagnoses and effective treatment plans. Traditional resources, such as medical books and services like UpToDate, are constrained by manual curation, ...

The potential, limitations, and future of diagnostics enhanced by generative artificial intelligence.

Diagnosis (Berlin, Germany)
OBJECTIVES: This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics.

Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.

Viruses
Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodolog...

AI-based differential diagnosis of dementia etiologies on multimodal data.

Nature medicine
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that har...

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Scientific reports
Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehe...

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis.

JMIR dermatology
BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders a...

Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors.

Scandinavian journal of gastroenterology
OBJECTIVES: Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this st...

Differentiating Gastrointestinal Stromal Tumors From Leiomyomas of Upper Digestive Tract Using Convolutional Neural Network Model by Endoscopic Ultrasonography.

Journal of clinical gastroenterology
BACKGROUND: Gastrointestinal stromal tumors (GISTs) and leiomyomas are the most common submucosal tumors of the upper digestive tract, and the diagnosis of the tumors is essential for their treatment and prognosis. However, the ability of endoscopic ...