Health

All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

339 stories · page 6 of 23

Evidence-backed gain

Deep Learning-Based Enhancement of Already Diagnostic-Quality MRI for Alzheimer's Disease Classification: Effects on Model Performance and Training Data Requirements

Background Deep learning (DL)-based image enhancement is widely used to improve suboptimal medical imaging. Whether it also benefits diagnostic-quality MRI in downstream task performance and data-efficiency remains unclear. Purpose To investigate the impact of DL-based enhancement applied to diagnostic quality structural MRI for Alzheimer's disease (AD) classification. Study type Retrospective. Population A total of 2293 brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were split into…

Journal of Magnetic Resonance Imaging · Health

Evidence-backed gain

Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status

Keratinocyte carcinoma (KC) places a considerable and growing burden on healthcare systems. Given the KC population's heterogeneity, tailored clinical pathways are needed to accommodate diverse management needs. This study applied machine learning (ML)-based phenomapping to identify distinct real-world subgroups within a national KC population using demographic and medical history variables. The study included KC patients treated in publicly-funded, office-based dermatology practices and registered in the Danish…

Health

Both readings

AI Simplification of Dermatopathology Reports for Patients: Basic Versus Prompt-Engineered Approaches

Background Patients struggle to comprehend dermatopathology reports. As artificial intelligence (AI) tools become more accessible, patients may use them to interpret reports; however, optimal approaches remain unexplored. Objective Evaluate whether prompt-engineered AI simplification of dermatopathology reports improves factualness, completeness, and reduces potential harm compared to basic AI usage. Methods Survey-based study (January-April 2025) of 52 US dermatology and dermatopathology professionals (70.3% re…

Health

Evidence-backed gain

PREDICTIVE OCT BIOMARKERS OF RETINAL CHANGES AND VISUAL OUTCOMES IN SILICONE OIL ENDOTAMPONADE IDENTIFIED BY ARTIFICIAL INTELLIGENCE

Purpose To quantify retinal layer changes and visual outcomes in eyes with silicone oil (SO) endotamponade for rhegmatogenous retinal detachment using OCT biomarkers and prediction models. Methods Seventy-six eyes with SO endotamponade underwent macular volume OCT at SO insertion and just before SO removal. An automated segmentation tool quantified retinal nerve fiber layer (RNFL), ganglion cell layer + inner plexiform layer (GCL+IPL), other retinal layers, and fluid (SRF, IRF). Eyes with and without macular ede…

Health

Evidence-backed gain

Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study

Objective To compare reading time, cancer detection rate (CDR), and abnormal interpretation rate (AIR) between AI-assisted and non-AI-assisted periods in screening and diagnostic mammography performed in routine clinical practice. Materials and methods We prospectively collected reading times for consecutive two-view full-field digital mammography interpreted by four radiologists between August 2023 and July 2024. Both screening and diagnostic examinations were included. A commercially available AI system was in…

Health

Both readings

Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations

Generative artificial intelligence (GAI) can be broadly described as an artificial intelligence system capable of generating images, text, and other media types with human prompts. GAI models like ChatGPT, DALL-E, and Bard have recently caught the attention of industry and academia equally. GAI applications span various industries like art, gaming, fashion, and healthcare. In healthcare, GAI shows promise in medical research, diagnosis, treatment, and patient care and is already making strides in real-world depl…

Health

Evidence-backed gain

CURL-AID: Automated Echocardiographic Motion Analysis for Quantitative Assessment of Posterior Systolic Curling

Objective Posterior systolic curling (PSC) is a morphofunctional abnormality of the posterior mitral annulus associated with malignant ventricular arrhythmias and sudden cardiac death. Current diagnosis is qualitative and operator-dependent, limiting reproducibility, objectivity and standardization. This study introduces CURL-AID (Curling Ultrasound-based Recognition and Labeling-Automated Intelligence-driven Diagnosis), a fully automated echocardiographic framework for PSC detection through quantitative analysi…

Health

Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro

Purpose Lumbar disc herniation is associated with substantial morbidity, including low back pain, radicular leg pain (sciatica), sensory disturbance, and motor deficit. Magnetic resonance imaging (MRI) is central to confirming the diagnosis in symptomatic patients and to planning surgical or interventional management. Recent advances in artificial intelligence (AI) raise the possibility of automating aspects of image interpretation to improve consistency and reduce radiologist workload. This study evaluates a ge…

Health

Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro

Advances in AI's Future in Toxicology: Integrating Computational Prediction and Clinical Translation Through Explainable Artificial Intelligence

Artificial intelligence (AI) and machine learning are increasingly used in toxicological risk assessment to predict chemical toxicity, identify hazardous compounds, and support regulatory decision-making. However, the widespread adoption of these models is limited by their "black-box" nature, which reduces interpretability, transparency, and regulatory confidence. Explainable artificial intelligence (XAI) has emerged as a promising approach to address these challenges by revealing how input features, including c…

Health

Additional sectioning and AI-assisted diagnosis reveal underdiagnosis of serous (pre)malignancies in fallopian tube specimen from BRCA1/2 carriers

Aim Germline BRCA1/2 pathogenic variant carriers are at increased risk for high-grade serous carcinoma (HGSC) and are therefore advised to have a risk-reducing salpingo-oophorectomy (RRSO) around the age of 40. A risk of 0.9% to develop peritoneal HGSC (pHGSC) remains, which increases up to 27.5% when serous tubal intraepithelial carcinoma (STIC) is detected at RRSO. The relationship between the detection of STIC and the occurrence of pHGSC is still poorly understood. Here, we investigated the role of tissue sam…

Health

Additional sectioning and AI-assisted diagnosis reveal underdiagnosis of serous (pre)malignancies in fallopian tube specimen from BRCA1/2 carriers

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns

Background Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. Objective To develop a novel oxidative stress index score integrating γ-glutamyl transferase and total bilirubin, evaluate its prognostic value in locally advanced rectal cancer patients receiving neoadjuvant therapy, and construct and validate machine learning-based survival prediction models incorporating oxidative stress index score. Design A novel liver enzyme indicator - oxidative stress index score…

Health

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns

Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider

Introduction Patients with musculoskeletal complaints often search online to identify an appropriate healthcare provider. With the increasing availability of large language models (LLMs), these artificial intelligence (AI) tools can direct patients to providers. This study evaluated the ability of LLMs to recommend appropriate providers based on representative patient musculoskeletal queries. Methods Three LLMs (ChatGPT, DeepSeek, and Gemini) were prompted with standardized musculoskeletal queries for two US cit…

Health

The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Diagnostic errors are a substantial source of patient harm. As artificial intelligence (AI) integrates into clinical workflows, opportunities are emerging to assess their impacts on diagnostic excellence (DxEx). The Coordinating Center for Diagnostic Excellence (CODEX) at the University of California San Francisco established the Action Incubator to translate research advances in DxEx into tangible strategies for improving diagnosis. The September 2025 in-person inaugural Action Incubator convened 30 multidiscip…

Health

The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda

Introduction Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance diagnostic efficacy, efficiency, and patient and provider safety. Methods We convened a two-day workshop with 18 interdisciplinary experts from three countries. Experts represented quality and patient safety, human factors and systems engineering, radiology and other medical specialties, nursing, medical informatics, cognitive and perceptual psychology, psychometri…

Health

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study

Background Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not experience clinically significant symptom reduction. Identifying these patients prior to the initiation of ICBT can support treatment planning. Objective The aim of this study was to enhance baseline prediction of clinically meaningful improvement in patients treated with ICBT for common psychiatric disorders in routine care, which could ultimately inform treatment plan…

Health