AIMC Topic: Adult

Clear Filters Showing 1781 to 1790 of 15606 articles

Clinical Application of AI in Mammography: Insights from a Prospective Study.

Academic radiology
RATIONALE AND OBJECTIVES: This prospective study evaluated the performance of AI in a diagnostic clinic setting, comparing its effectiveness with radiologists of varying experience.

Machine Learning-Assisted Prediction of Persistent Incomplete Occlusion in Intracranial Aneurysms From Angiographic Parametric Imaging-Derived Features.

Academic radiology
RATIONALE AND OBJECTIVES: To develop machine-learning (ML) models incorporating angiographic parametric imaging (API)-derived parameters in predicting persistent incomplete occlusion of intracranial aneurysms (IAs) after flow diverter (FD) treatment.

A Machine Learning Trauma Triage Model for Critical Care Transport.

JAMA network open
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...

Efficiency and Quality of Generative AI-Assisted Radiograph Reporting.

JAMA network open
IMPORTANCE: Diagnostic imaging interpretation involves distilling multimodal clinical information into text form, a task well-suited to augmentation by generative artificial intelligence (AI). However, to our knowledge, impacts of AI-based draft radi...

Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.

JAMA network open
IMPORTANCE: Clinical artificial intelligence (AI) systems are susceptible to performance degradation due to data shifts, which can lead to erroneous predictions and potential patient harm. Proactively detecting and mitigating these shifts is crucial ...

Accuracy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care.

JAMA network open
IMPORTANCE: Integrating artificial intelligence (AI) technologies into gatekeeping holds significant potential, as it efficiently handles repetitive tasks and can process large amounts of information quickly.

Assessing medical students' readiness for artificial intelligence after pre-clinical training.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is becoming increasingly relevant in healthcare, necessitating healthcare professionals' proficiency in its use. Medical students and practitioners require fundamental understanding and skills development to m...

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Scientific reports
Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy, neuropathy, foot problems, heart attacks, and strokes if left unchecked. Prompt detection and diagn...

Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.

JMIR medical informatics
BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.

Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settings.

EBioMedicine
BACKGROUND: Generalisation of artificial intelligence (AI) models to a new setting is challenging. In this study, we seek to understand the robustness of a dermatology (AI) model and whether it generalises from telemedicine cases to a new setting inc...