Identifying and Reporting Dependent Adult abuse

Enhancing fairness and standardization in AI-versus-physician diagnostic comparisons: A scoping review.

OBJECTIVE: The growing number of studies directly comparing artificial intelligence (AI) to physicians in diagnostic tasks often focuses on performance outcomes, overlooking fundamental methodological rigor. This scoping review aims to critically appraise the methodological quality of this body of literature, identifying key challenges and proposing a framework to enhance the fairness, standardiza...

Molecular Stratification of Antiphospholipid Syndrome Through Integrative Analysis of the Whole-Blood RNA Transcriptome.

OBJECTIVE: Antiphospholipid syndrome (APS) is a thromboinflammatory disorder characterized by clinical and mechanistic heterogeneity that complicates early diagnosis and hinders targeted treatment. We aimed to identify distinct molecular endotypes among antiphospholipid antibody (aPL)-positive patients using whole-blood transcriptomics. METHODS: Whole-blood RNA sequencing was performed on 174 aPL-...

Beyond handcrafted radiomics in oncologic imaging: Innovations in deep, explainable, multi-site and multi-omics radiomics approaches.

Radiomics seeks to convert medical images into quantitative biomarkers capable of capturing tumor phenotype, microenvironment, and underlying biology....

Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial.

OBJECTIVES: Non-contrast MRI (bi-parametric MRI-bpMRI) has been investigated as a potential tool to be integrated in clinically significant prostate c...

Predicting vesicoureteral reflux outcomes using artificial intelligence: A critical appraisal using APPRAISE-AI.

Vesicoureteral reflux (VUR) is a common congenital urinary tract anomaly in children, associated with recurrent urinary tract infections (UTIs) and lo...

Postmarketing Safety of Transcranial Magnetic Stimulation: A 10-Year MAUDE Database Analysis of Adverse Events and Technological Advancements.

Background: Transcranial magnetic stimulation (TMS) is an FDA-cleared neuromodulation technique with expanding clinical applications beyond major depr...

Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.

BACKGROUND: Depression affects people's daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language proce...

A physics-informed dual-branch fusion network for quantitative determination of total phosphorus in water using near-infrared spectroscopy.

BACKGROUND: Quantitative determination of total phosphorus (TP), an indirectly absorbing aquatic indicator, using near-infrared (NIR) spectroscopy is ...

Understanding complaint behavior in mobile banking: A psychological and AI-based analysis of emotional drivers.

This study investigates how emotional intelligence and AI techniques can classify user-reported issues in retail mobile banking apps and examine the a...

A Multimodal Fusion Model of Radiomics and Deep Learning Integrating the Tumor Microenvironment Accurately Predicts Pathological Complete Response in Breast Cancer.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a critical prognostic marker in breast cancer, yet its predictio...

The Burden of Delayed Diabetic Retinopathy Management and Use of Artificial Intelligence-Driven Screening Tools: A Systematic Literature Review.

PURPOSE: Patients with diabetic retinopathy (DR) are at risk of visual deterioration owing to systemic and financial barriers in accessing appropriate...

Classifying Delayed Dental Care Using Machine Learning: A National Health Interview Survey Analysis.

BACKGROUND: Delays in dental care worsen oral disease and mirror broader inequities in health care access and use. OBJECTIVE: To estimate the 12-month...

Maximizing impact of explainable artificial intelligence in radiotherapy: a critical review.

Objective.Artificial intelligence (AI) can enable automation, improve treatment accuracy, allow for a more efficient workflow, and improve the cost-ef...

Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.

BACKGROUND: Warfarin remains one of the most widely used anticoagulants; however, its narrow therapeutic index means that even small dosing deviations...

Evaluating the role of ChatGPT in structured radiology reporting: A systematic review.

BACKGROUND: The integration of large language models (LLMs) such as ChatGPT into radiology has introduced new possibilities for structured reporting. ...

Evaluating guideline adherence in LLM studies using LLMs.

PURPOSE: To evaluate the capability of large language models (LLM), specifically GPT-4 and o1, in assessing adherence to the MI-CLEAR-LLM checklist in...

Prediction models for maltreatment risk: TRIPOD/PROBAST compliance, calibration, and fairness-A systematic review.

BACKGROUND: Prediction models for child maltreatment risk are increasingly used to support decisions in child protection, yet concerns remain about me...

[Innovative imaging techniques for urothelial carcinoma].

BACKGROUND: Imaging guides the staging of urothelial carcinoma and thus plays a central role in treatment decisions. Current guidelines reflect the in...

Machine learning-based COVID-19 prognostic models lag behind in reporting quality: findings from a TRIPOD/TRIPOD + AI systematic review.

BACKGROUND: Reporting of COVID-19 prognostic models frequently falls short of established standards. The TRIPOD checklist and its 2024 AI extension (T...

Bias and Oversight in Clinical AI: A Review of Decision Support Tools and Equity Frameworks.

Artificial intelligence (AI) decision support tools (DSTs) are increasingly used across clinical settings to improve efficiency and support decision-m...