AIMC Topic: Mass Screening

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Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening: Development and Usability Study.

JMIR formative research
BACKGROUND: The evolution of language models, particularly large language models, has introduced transformative potential for psychological assessment, challenging traditional rating scale methods that have dominated clinical practice for over a cent...

Utilizing AI CAD for early pandemic screening in chest radiographs.

Scientific reports
To investigate the potential application of existing artificial intelligence (AI) software in diagnosing COVID-19 (coronavirus disease 2019) and other pneumonia-related radiographic findings with the unprecedented challenge by COVID-19 pandemic, leve...

Machine learning for screening laryngopharyngeal reflux symptoms in college students: a cross-sectional study.

Annals of medicine
BCKGROUND: Laryngopharyngeal reflux (LPR) is a widespread global health issue. Its recurring symptoms and impact on quality of life create significant economic burdens for individuals and society. To examine the links between lifestyle, diet, and LPR...

Construction of an intelligent screening model for allergic rhinitis based on routine blood tests.

PloS one
The incidence of allergic rhinitis (AR) has been increasing annually, severely impacting patients' quality of life and increasing socioeconomic burdens. The limitations of current diagnostic methods have made the development of efficient, low-cost ea...

Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.

JMIR medical informatics
BACKGROUND: Depressive episodes in bipolar disorder are frequent, prolonged, and contribute substantially to functional impairment and reduced quality of life. Therefore, early and objective detection of bipolar depression is critical for timely inte...

Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.

Scientific reports
Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtypi...

Reach and implementation of human and AI-assisted diabetic retinopathy screening models in primary healthcare settings in India.

Scientific reports
Diabetic retinopathy (DR) is a leading cause of preventable vision loss. While DR screening is critical, evidence on the reach and implementation of different screening models in primary healthcare settings is limited. This study evaluated the reach ...

Development and application of a deep learning-based tuberculosis diagnostic assistance system in remote areas of Northwest China.

Scientific reports
The Kashgar region, located in Northwest China, has a significantly higher incidence of tuberculosis (TB) compared to the national average. Local governments conduct annual TB screening using medical imaging. However, due to a shortage of radiologist...

Describing the Performance and the Infrastructure Requirements of the Existing Artificial Intelligence (AI)-Based Diabetic Retinopathy (DR) Screening Algorithms for Diabetic Patients: an Umbrella Review.

Journal of medical systems
AI-based diabetic retinopathy (DR) screening algorithms have been evaluated in many countries and have shown promise in expanding access to screening, especially in low- and middle-income countries (LMICs). However, the literature lacks guidance on w...