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877 published stories · page 45 of 59

Evidence-backed gain

Den-SOFA: Dental Student Outcome Forecasting Assistant using explainable machine learning models

Artificial intelligence (AI) and machine learning (ML) are increasingly being explored in dental education to support academic assessment and identify students at risk of poor performance. However, predictive modeling in this setting remains challenging because complex, nonlinear relationships among academic and demographic variables influence student achievement. Moreover, the value of such models lies not only in prediction but also in interpretability. This study evaluated Den-SOFA, an explainable ML framewor…

BMC Medical Education · Education

Den-SOFA: Dental Student Outcome Forecasting Assistant using explainable machine learning models

Evidence-backed gain

Effect of AI-assisted caries annotation on dental students' performance in caries detection on panoramic radiographs

Dental caries remains one of the most prevalent oral diseases globally. The integration of artificial intelligence (AI) into dental radiographic interpretation, particularly for caries detection, has expanded rapidly. AI-assisted caries annotation may help dental students identify carious lesions on panoramic radiographs-a commonly used diagnostic tool for evaluating teeth and surrounding structures-thereby improving diagnostic accuracy, efficiency, and confidence. This study aimed to assess the effect of AI-ass…

Health

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AI-Assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation

Cardiomegaly screening via manual Cardiothoracic Ratio (CTR) measurement remains a clinical bottleneck, while contemporary deep learning solutions often suffer from algorithmic bloating. To address the need for resource-efficient and interpretable triage, this study proposes a framework driven by implicit morphological inference, which bypasses the requirement for explicit heart segmentation. We developed UBNet-Seg, a lightweight U-Net variant (2.3 million parameters) trained on a heterogeneous dataset of 11,748…

Health

Evidence-backed gain

Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography

Aim To develop and evaluate the diagnostic accuracy of deep learning (DL) models in differentiating keratoconus (KC) from normal eyes with regular astigmatism. Methods A comparative cross-sectional study was conducted at the Cornea and Diagnostic Department of Al-Shifa Trust Eye Hospital, Pakistan. Galilei dual Scheimpflug-based corneal topography was performed to obtain four corneal maps: anterior axial curvature, posterior axial curvature, corneal thickness, and posterior elevation. Four convolutional neural n…

Health

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Advancements in machine learning and deep learning for early detection and management of mental health disorder

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues. The development of ML and DL methods for the early diagnosis and treatment…

Health

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Relationships in the age of AI: A review on the opportunities and risks of synthetic relationships to reduce loneliness

Loneliness is a pressing global health issue, yet traditional interventions often fall short due to scalability limitations and the individualized experiences of loneliness. The rise of generative artificial intelligence (AI) has enabled synthetic relationships (SRs)—ongoing associations with AI companions designed to simulate human-like social bonds. SRs offer, among other aspects, constant availability, adaptability, and emotional responsiveness, which potentially address loneliness. However, their growing int…

Lifestyle

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Co-designing AI systems with value-sensitive citizen science

Abstract As AI systems increasingly influence everyday life, integrating diverse community values is both ethically essential and practically urgent. This paper presents Value-Sensitive Citizen Science (VSCS)—a systematic framework that combines Value-Sensitive Design (VSD) with citizen science to support meaningful public participation in AI development. VSCS addresses gaps in the existing approaches by integrating culturally grounded methods and cognitive scaffolding through the Participatory Value-Cognition T…

Policy

A deep learning framework for efficient pathology image analysis

Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature…

Health

A deep learning framework for efficient pathology image analysis

A framework for developing university policies on generative AI governance: a cross-national comparative study

As generative AI (GAI) becomes increasingly embedded in higher education, universities worldwide are developing policies to govern its ethical, pedagogical, and institutional use. However, these policies vary across national and institutional contexts. We undertake a cross-national analysis of GAI guidelines issued by leading universities in the United States, Japan, and China, identifying key policy orientations and proposing a structured framework to support policy development. Using an extended Technology Acc…

Policy

A framework for developing university policies on generative AI governance: a cross-national comparative study

Phenotyping antidepressant treatment response with deep learning in electronic health records

ABSTRACT Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applications but remains a challenge. Increasingly, artificial intelligence methods using “deep learning” applied to clinical data have shown promise in complex classification problems. Here, we systematically evaluate the performance of eight deep-learning-based natural language processing models in classifying response to antidepressants in a large real-world…

Health

Large language models are powerful electronic health record encoders

Electronic health records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specific electronic health record foundation models trained on unlabeled EHR data have shown improved predictive accuracy and generalization. However, their development is constrained by limited data access and site-specific vocabularies. We convert EHR data into plain text by replacing medical codes with natural-language descriptions, enabli…

Health

A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks

Abstract Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, su…

Health

A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks

Algorithmic Justice and Responsible AI Journalism: A Comparative Communication Policy Perspective in East Asia

As generative artificial intelligence and automated content curation rapidly reshape the global media landscape, the intersection of algorithmic justice and media governance has become a critical frontier for sustainable development. This study provides a comparative communication policy analysis of China and South Korea, focusing explicitly on how their distinct regulatory toolkits address the tension between algorithmic fairness and platform accountability in AI journalism. While China utilizes a top-down, sta…

Policy

Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

Nasopharyngeal carcinoma (NPC) is rare in Europe, and emerging data suggest poorer outcomes in Caucasian patients compared with Asian populations, highlighting the need for region-specific prognostic tools. Inflammation-based biomarkers and artificial intelligence show promise for risk stratification and prediction of survival and second primary cancers (SPCs). We conducted a retrospective multicentre study including 405 NPC patients from six European institutions. Demographic, clinicopathological, and haematolo…

Health

Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy

Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study

Protein-energy wasting (PEW) is common in incident hemodialysis patients and linked to poor outcomes. The uric acid/HDL-cholesterol ratio (UHR) and intact parathyroid hormone (iPTH) relate to metabolic, inflammatory, and nutritional disturbances, but their value for predicting PEW in incident hemodialysis is unclear. This retrospective multicenter study included 863 incident hemodialysis patients. PEW was defined according to the International Society of Renal Nutrition and Metabolism criteria. UHR and iPTH were…

Health