AIMC Topic: Humans

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Tumor microenvironment expressed enzymes (Matrix metalloproteinases, cathepsins, urokinase-type plasminogen activator) triggered polymersomes for liquid biopsy and cancer diagnostics: A review.

International journal of biological macromolecules
Enzyme-triggered polymersomes have emerged as a transformative platform in liquid biopsy and cancer diagnostics, enabling high-accuracy biomarker detection through precise enzymatic responsiveness. This review comprehensively examines the rational de...

Generative AI-Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial.

JMIR formative research
BACKGROUND: Colleges have turned to digital mental health interventions to meet the increasing mental health treatment needs of their students. Among these, chatbots stand out as artificial intelligence-driven tools capable of engaging in human-like ...

Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. Misdiagnosis might lead to several systemic conditions, including hypertension, vascular damage, and cognitive impairment. The gold-standard diagnostic tool for OSA is po...

Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.

BMC medical imaging
BACKGROUND: Recent advances in histology scanning technology and Artificial Intelligence (AI) offer great opportunities to support cancer diagnosis. The inability to interpret the extracted features and model predictions is one of the major issues li...

Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying endotracheal tube position on plain chest X-ray: a multi-case multi-reader study.

Critical care (London, England)
BACKGROUND: Incorrectly placed endotracheal tubes (ETTs) can lead to serious clinical harm. Studies have demonstrated the potential for artificial intelligence (AI)-led algorithms to detect ETT placement on chest X-Ray (CXR) images, however their eff...

Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.

BMC medical informatics and decision making
INTRODUCTION: Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages...

The impact of artificial intelligence (AI) on maternal mortality: evidence from global, developed and developing countries.

Globalization and health
BACKGROUND: This study examines the impact of Artificial Intelligence (AI) on maternal mortality in alignment with Sustainable Development Goal (SDG) 3.1, which aims to reduce maternal mortality to below 70 per 100,000 live births by 2030. Despite ad...

Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive geriatric assessment.

BMC medical informatics and decision making
BACKGROUND: Lumbar spinal stenosis is one of the most common surgery-requiring conditions of the spine in the aged population. As elderly patients often present with multiple comorbidities and limited physiological reserve, individualized risk assess...

A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei.

BMC medical imaging
OBJECTIVE: This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (P...