Surveys

From Principles to Action: A French Framework for Operationalizing Ethics in Health AI.

Artificial intelligence (AI) is rapidly transforming healthcare but raises ethical concerns. To address these, the French Delegation for Digital Health developed a practical implementation guide to help manufacturers operationalize ethical principles in health AI systems. Developed between 2023 and 2025 through a collaborative process, the guide defines 43 actionable criteria covering the entire A...

Development Parameters of the Decision Aid Rule-Based Evaluation and Support Tool (REST) for Optimizing the Resources of an Information Extraction Task.

We aimed to develop and test a decision aid tool for NLP agnostic developers to assess the most relevant information extraction (IE) method. Because of non-inferior IE performance under specific conditions and of better carbon footprint, transferability and interpretability, we set up rules as the default IE method of the REST tool. We hypothesized that a hybrid IE method might optimize both rules...

Building a Silver-Standard Dataset from NICE Guidelines for Clinical LLMs.

Large language models (LLMs) are increasingly used in healthcare, yet standardised benchmarks for evaluating guideline-based clinical reasoning are mi...

Redesigning Python Programming Course in the Generative AI Era: An Italian Case Study.

This study presents the redesign of a Python programming course for medical students at the University of Pavia, integrating the Generative AI (GenAI)...

Overcoming the Limitations of Standardized Patients Through Dynamic CPX Scenario Automation and Multimodal Clinical Representation.

This study addresses the limitations of Standardized Patients (SPs) in Traditional Korean Medicine (TKM) by developing an AI-driven CPX platform. By i...

Context-Free Grammar-Guided Generation of FHIR Resources Using Large Language Models.

Large Language Models (LLMs) have shown remarkable capabilities in medical information extraction and data transformation tasks. However, their unstru...

Risk prediction of depression- and anxiety-related psychological distress in a large multistage survey sample of Chinese adults: a multi-algorithm ensemble machine-learning model with SHAP.

OBJECTIVE: To develop an interpretable ensemble machine-learning model to support risk stratification of elevated depression- and anxiety-related psyc...

Artificial intelligence and clinical informatics in UK ophthalmology training: a national cross-sectional survey.

BACKGROUND: Artificial intelligence (AI) and clinical informatics (CI) are strategic priorities for UK ophthalmology, with the Royal College of Ophtha...

LFaB: Low Fidelity as Bias for Active Learning in the Chemical Configuration Space.

Active learning promises to provide an optimal training sample selection procedure in the construction of machine learning models. It often relies on ...

Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.

BACKGROUND: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging...

Defining the artificial intelligence knowledge gap in surgery: experience and perspectives from surgical resident and postgraduate.

Despite the growing prominence of Artificial Intelligence (AI) in surgical practice, surgical residents and postgraduates receive limited formal train...

Mobile Learning in Medical Education: Quasi-Experimental Realist Evaluation of Usage, Context, and Examination Performance in a Curricular Setting.

BACKGROUND: Mobile learning (mLearning) is widely used in medical education. Previous research has focused on technology acceptance and intervention e...

Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer morbidity and mortality worldwide. The complexity of guideline-concordant care and un...

Hallucination risk and trustworthiness of generative AI systems based on IVMPF-MABAC decision-making strategies.

Hallucination risk and trustworthiness of a generative artificial intelligence system play an important and vital role in daily-life scenarios. The te...

Assessing and mitigating traffic crash risks using interpretable machine learning techniques.

This study aims to examine factors associated with self-reported crash involvement among drivers in Pakistan using interpretable machine learning (ML)...

Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19.

Food insecurity remains a critical global challenge, with low-income countries such as Ethiopia bearing a disproportionate burden. In settings where f...

Prediction of unerupted canines and premolars widths in an Emirati population: development and validation of regression and machine learning models.

This study aimed to develop a more accurate model for predicting the widths of unerupted canines and premolars in Emirati children, using deep learnin...

Artificial intelligence to revolutionize surgical decision-making is still just around the corner.

Medical artificial intelligence, especially large language models, has engendered both excitement and unease across the medical community, promising i...

Mitigating Algorithmic Bias in AI-Powered Toxicology: Frameworks for Explainable and Equitable Predictions in Human Health and Environmental Safety.

The rapid integration of artificial intelligence (AI) and machine learning into predictive toxicology has transformed chemical hazard identification, ...

Automated scoring of student videos in medical education: a comparison between a large language model and expert evaluation.

Video-based assignments are used in medical education, yet expert scoring is time-intensive. Large language models (LLMs) offer scalable alternatives,...