Health

626 stories · page 11 of 42

HealthBoth readings

Assessing the accuracy and usability of artificial intelligence-based language models in responding to common periodontal patient questions

Background Artificial intelligence-powered large language models (LLMs) are increasingly used by patients seeking quick information regarding dental and medical problems. Despite their growing popularity, concerns remain regarding the accuracy, clarity, and clinical usefulness of LLM-generated responses. This study aimed to comparatively evaluate the accuracy and usability of widely used LLMs in responding to frequently asked periodontal questions. Methods In this analytical-comparative study, 15 commonly asked…

Clinical Advances in Periodontics

Evidence-backed gain

Deep Learning-Powered Plasmonic Platform for Decoding Dynamic Protein Aggregation Landscapes in Parkinson's Disease Progression

Evidence-backed gain

Development of an Explainable Machine Learning Model for Predicting Reflux Esophagitis Among Candidates for Metabolic Bariatric Surgery

Evidence-backed problem

Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases

Both readings

Constructing Legal Knowledge Graphs and Tracing Bias in Medical Artificial Intelligence Based on Graph Neural Networks

In medical artificial intelligence (AI) legal compliance analysis, this paper addresses the ambiguity in liability determination caused by the semantic fragmentation of multi-source heterogeneous legal texts and difficulties in tracing algorithmic biases. It constructs a knowledge graph construction and bias propagation path detection method based on graph neural networks. Four types of heterogeneous nodes-legal clauses, medical behaviors, algorithm modules, and responsible parties-are structured and encoded, an…

Both readings

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study

Background Enfortumab vedotin (EV) therapy for advanced urothelial carcinoma is limited by adverse events (AEs). Early identification of high-risk patients is needed. This proof-of-concept study evaluated whether machine learning (ML) with explainable AI (SHAP) could predict EV toxicities using real-world data. Research design and methods Data from 542 patients (51 centers, 24 countries) were analyzed. Six outcomes were predicted including grade 3-4 AEs. Four ML algorithms were trained on an 80% split and tested…

Both readings

Can Artificial Intelligence Deliver in Real-World Health Systems? Early Insights From Augmented Intelligence in Medicine and Healthcare Initiative's 5 Funded Projects

Introduction Artificial intelligence (AI) holds tremendous promise to improve clinical decision-making across diagnosis, risk assessment, and patient care. However, most prior work has focused on model development in controlled settings with limited evidence on real-world implementation. The Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI), led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, was established to evaluate and support integration of AI tools into routin…

Both readings

Quality of Online Information for Management of Leg Pain in Children and Adolescents With Hypermobility-Associated Conditions

Aim Online health information seeking continues to gain popularity. Quality assessment of online information is particularly important for patient populations with low satisfaction and disengagement from health professionals and services, such as hypermobility-associated conditions. We aimed to assess the quality of online health information, for management of lower limb pain in children with hypermobility-associated conditions, available through search engines and corresponding AI-generated overviews. Methods W…

Health

Decoding the Bone-Eye Axis: Machine Learning for Age-Related Macular Degeneration Risk Prediction

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss, yet systemic determinants of its risk remain incompletely understood. Bone mineral density (BMD), a marker of skeletal and biological aging, may reflect shared pathways linking systemic and retinal degeneration. We investigated the association between BMD and AMD using a multilayered framework integrating epidemiological analyses, Mendelian randomization (MR), proteomic and metabolomic profiling, machine learning, and an exper…

Health

A hybrid deep learning framework for early autism screening

Objectives Early diagnosis of autism spectrum disorder (ASD) remains a significant challenge due to the time-consuming and subjective nature of traditional diagnostic methods. This study proposes a reliability-oriented hybrid deep learning framework that provides a low-cost, scalable, non-invasive, and AI-assisted pre-screening tool for early ASD risk indication and referral support. Methods The proposed system integrates two independent deep learning architectures: (1) a ResNet18 model optimized with 10-fold cr…

Health

Determination of candidate predictors for chiropractic treatment outcome of spinal pain using a machine learning framework for small datasets

Objectives To develop a machine learning (ML) approach to explore self-reported factors predictive for recovery in a small set of spinal pain patients. Methods In this prospective cohort study, patients ( N = 96; mean age = 44.5 ± 16.5 years; 53 female) completed an extensive questionnaire at baseline and after 1 and 3 months. Prediction targets were defined as binary outcomes (recovery/non-recovery) based on improvement at 3 months for pain intensity, disability, quality of life, and Patient Global Impression o…

Health

Is It Feasible to Assess Clinical Remission in Asthma via a Customized Data Extraction Approach from a Real-World Clinical Electronic Medical Records Database in Japan? A Retrospective Real-World Study

Objective : Clinical remission (CR) is an ambitious and attainable treatment goal for patients with asthma. This retrospective, observational study determined the feasibility of assessing CR using an electronic medical record (EMR) database. Methods : Data from the JAMDAS database were analyzed for patients with asthma initiating fluticasone furoate/umeclidinium/vilanterol single-inhaler triple therapy between August 18, 2020 and December 31, 2024. Asthma Control Test (ACT) scores were extracted using structured…

Health

Retrospective comparison of three commercial artificial intelligence algorithms for detection of intracranial hemorrhage (ICH) in the emergency radiology department

BackgroundSeveral commercial artificial intelligence (Al) algorithms are available for detecting intracranial hemorrhage (ICH), but independent clinical validation remains limited.PurposeTo compare three commercially available Al algorithms for ICH detection on non- contrast head computed tomography (NCHCT).Material and MethodsIn this retrospective study, 4027 consecutive NCHCT examinations from a large emergency hospital in southwest Sweden were analyzed. Three Al algorithms were applied, with one vendor disclo…

Health

Artificial Intelligence at the Boundary of Spiritual Experience and Psychopathology: A Scoping Review of Evidence from Türkiye, the USA, the UK, Poland, South Korea, and Israel

The rapid integration of artificial intelligence (AI) into everyday life has created new intersections among digital technology, spirituality, mystical experience, and psychopathology. This scoping review aimed to map evidence on how AI mediates, reinforces, detects, predicts, or interprets religious and spiritual experiences at the boundary with psychosis and dissociation. PubMed, Web of Science, Embase, the Cochrane Library, and Scopus were searched through June 30, 2026, with supplementary reference-list scre…

Health

Radiomics and dosiomics in radionecrosis prediction in brain metastasis treated with Stereotactic Radiation Therapy: a machine learning approach

Introduction Stereotactic Radiotherapy plays a main role in Brain Metastases treatment. Radiomics and Dosiomics, coupled with Machine Learning approaches are emerging in radiation oncology as support in clinical decision-making workflow. In this study a machine learning approach was used to predict the late toxicities induced by Stereotactic Radiotherapy including clinical, radiomics and dosiomics features extracted from patients. Materials and methods Lesions contribution, concomitant and/or sequential treatmen…