AIMC Topic: Middle Aged

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Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose.

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

Health care management science
BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in healthcare in America. For cancer specifically, one of the leading observed disparities is worse mortality among non-Hispanic Black patients compared to n...

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Neurology
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific eff...

Artificial Intelligence and Radiologist Burnout.

JAMA network open
IMPORTANCE: Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI.

Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure.

JAMA network open
IMPORTANCE: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials.

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models.

European journal of medical research
OBJECTIVES: Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.

Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.

Scientific reports
Visual scoring of interstitial lung disease in systemic sclerosis (SSc-ILD) from CT scans is laborious, subjective and time-consuming. This study aims to develop a deep learning framework to automate SSc-ILD scoring. The automated framework is a casc...

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.

Frontiers in cellular and infection microbiology
BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in d...

Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews.

Frontiers in public health
BACKGROUND: Progress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI's ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the i...

Common laboratory results-based artificial intelligence analysis achieves accurate classification of plasma cell dyscrasias.

PeerJ
BACKGROUND: Plasma cell dyscrasias encompass a diverse set of disorders, where early and precise diagnosis is essential for optimizing patient outcomes. Despite advancements, current diagnostic methodologies remain underutilized in applying artificia...