Health

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Identifying key risk factors of adolescent Internet Gaming Disorder using explainable machine learning and network analysis

Background Internet Gaming Disorder (IGD) poses significant psychological and social risks for adolescents. Its development involves complex interactions among early adversity, negative life events, psychological symptoms, and regulatory emotional self-efficacy, yet relative importance of these factors and their interrelationships remain incompletely understood. Objective To identify the key demographic and psychosocial factors associated with adolescent IGD, quantify their relative contributions to IGD risk, an…

Journal of Affective Disorders

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Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study

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Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC

$A multi-task learning-based deep learning model for precise estimation of rib fracture age on chest CT$

Evidence-backed gain

A multi-task learning-based deep learning model for precise estimation of rib fracture age on chest CT

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Artificial intelligence in laboratory medicine: From machine learning to large language models

Abstract The integration of artificial intelligence (AI) into laboratory medicine has undergone a remarkable evolution over the past decade, shifting from traditional machine learning (ML) applied to structured laboratory data to the recent emergence of large language models (LLMs) capable of processing unstructured clinical text, interpreting complex laboratory findings, and supporting clinical decision-making. This narrative review traces that progression and critically compares four main AI paradigms accessib…

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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…

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The future landscape of large language models in medicine

Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public attention after OpenAI's ChatGPT was made publicly available in November 2022. LLMs can often answer questions, summarize, paraphrase and translate text on a level that is nearly indistinguishable from human capabilities. The possibility to actively interact with models like ChatGPT makes LLMs attractive tools in various fields, including medicine. While these m…

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…

Health

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-…

Health

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…

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…

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…

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…

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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…