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1289 published stories · page 17 of 86

Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias
HealthEvidence-backed gain

Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias

Background Hospitals are increasingly adopting artificial intelligence and machine learning (AI/ML) tools to support clinical decision making and care management. However, evidence on how hospital AI/ML adoption relates to inpatient utilization and spending among clinically complex populations remains limited. Older adults with Alzheimer's disease and related dementias (ADRD) experience higher rates of readmissions and potentially avoidable hospitalizations. Methods Cross-sectional study was conducted using 2023…

Journal of the American Geriatrics Society
Comprehension of an AI-generated discharge letter versus a traditional discharge letter: two controlled quasi-experimental parallel-group studies
Evidence-backed gain

Comprehension of an AI-generated discharge letter versus a traditional discharge letter: two controlled quasi-experimental parallel-group studies

Background Discharge letters are essential for continuity of care, yet patients often misunderstand their content. Poor discharge communication is associated with medication errors, inappropriate healthcare use, and hospital readmissions. Artificial intelligence (AI) language models may improve discharge documentation clarity and accessibility. This study aimed to compare a GPT-4-generated, accessibility-optimised discharge letter with a conventional discharge letter regarding comprehension of key discharge info…

Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence
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Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence

Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise routinely mitigates potential issues due to quality variability into or across laboratories, ensuring diagnostic accuracy. However, as artificial intelligence (AI) induced by whole slide imaging (WSI) emerges as a future advancement in pathology, a shift toward greater technical standardisation of the preparations or adaptive algorithms deserves consideration. This study examined longitudinal data (2019-…

Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning
Evidence-backed gain

Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning

Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Compared to single-chemical indicators, biomarkers more sensitively reflect biological effects and early ecological risks. To address these issues, a method for constructing a comprehensive index integrating biological and non-biological multi-indexes was proposed in this study. First, the Criteria Importance Through Intercriteria Correlation (CRITIC)-Fuzzy Biomarker Response Index (CFBRI) is developed by i…

Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs
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Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs

Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains challenging. Objective To develop and evaluate an artificial intelligence model for detecting pneumothorax on supine neonatal chest radiographs. Materials and methods This retrospective single-center study included neonates admitted to the neonatal intensive care unit between January 2011 and December 2024. The dataset comprised 648 radiographs from 288 neonates with pneumothorax and 5,511 radiographs from 3,…

Health

Nucleic Acid Turnover and Lipid Remodeling Distinguish T Cell Activation and Exhaustion States via Label-Free Raman Spectroscopy

T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including tuberculosis, HIV, malaria, and cancer, yet current detection methods require expensive and tedious antibody labeling, destructive workflows, or days-long functional assays that limit dynamic monitoring capabilities. Here, we introduce Raman spectroscopy as a label-free assay for distinguishing T cell states directly from culture while preserving viability for downstream use. We leverage a 1-D convolutio…

Nucleic Acid Turnover and Lipid Remodeling Distinguish T Cell Activation and Exhaustion States via Label-Free Raman Spectroscopy
Health

Interpretable machine learning-enabled risk stratification for pulmonary arterial hypertension associated with unrepaired congenital heart disease: results from a national prospective registry

Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated with unrepaired congenital heart disease (PAH-CHD). We aimed to develop and internally validate an interpretable machine learning (ML)-based risk model for adults with unrepaired PAH-CHD, comparing it against the recent European Society of Cardiology (ESC) model. Methods Utilizing a nationwide prospective registry in China, we included adults with unrepaired PAH-CHD. Five survival models were trained and i…

Interpretable machine learning-enabled risk stratification for pulmonary arterial hypertension associated with unrepaired congenital heart disease: results from a national prospective registry
Health

Augmented and mixed reality as auxiliary tools in dental procedures and their integration with artificial intelligence: a scoping review

Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixed Reality (MR) as auxiliary tools in dental procedures, and to examine the extent to which Artificial Intelligence (AI) has been integrated into these workflows. Data A scoping review was conducted in accordance with the PRISMA-ScR guidelines and the Joanna Briggs Institute (JBI) recommendations. The study selection and data extraction processes were carried out by two independent reviewers, with disagree…

Augmented and mixed reality as auxiliary tools in dental procedures and their integration with artificial intelligence: a scoping review
Health

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease

Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. Methods For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or…

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease
Health

Current state of research and future developments of artificial intelligence in pain diagnosis and treatment

Chronic pain represents a significant global health challenge, with traditional diagnostic and therapeutic approaches facing limitations in objective assessment and precision treatment. Advances in artificial intelligence (AI) technology have opened new avenues for addressing these challenges. This review systematically examines the current state and future trends of AI research in the field of pain medicine. The article focuses on three core research directions: In pain assessment, studies concentrate on levera…

Current state of research and future developments of artificial intelligence in pain diagnosis and treatment
Health

Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era

Objective Breast cancer (BC) is a worldwide health task, and its increasing frequency and the associated burden on healthcare professionals are also increasing. Hence, integrating machine learning (ML) and artificial intelligence (AI) into screening and diagnosis is irrefutable in the digital age. A comprehensive systematic review was conducted aimed at assessing the application of AI and ML techniques to enhance BC screening, diagnosis, classification, and tumor marker scoring. Methods An electronic literature…

Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era
Health

Artificial Intelligence-Enabled Orthodontic Care for Remote and Underserved Populations: A Scoping Review of Access, Technology, and Public Health Integration

Background Unequal access to orthodontic care remains a major public health challenge, particularly in rural and underserved regions, recent advancements in artificial intelligence (AI) and digital orthodontics offer potential to bridge these gaps through remote diagnosis and monitoring. Aim This scoping review aims to systematically map and categorize how AI applications in orthodontic diagnosis, treatment planning, and remote monitoring enhance access to care for underserved and remote populations while identi…

Artificial Intelligence-Enabled Orthodontic Care for Remote and Underserved Populations: A Scoping Review of Access, Technology, and Public Health Integration