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1357 published stories · page 37 of 91

HealthEvidence-backed gain

Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data

Objectives Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treatment. Accurate prognostic feature identification is critical for personalized therapy and patient survival improvement, while traditional indicators and single biomarkers lack sufficient accuracy in prognostic prediction. Methods In this population-based retrospective study, we analyzed 5392 patients with cervical squamous cell carcinoma from the surveillance, epide…

American Journal of Clinical Oncology

Both readings

Artificial intelligence-driven transformation in healthcare: the mediating role of technostress in the impact of openness to organizational change and attitude toward AI on innovative behavior

Evidence-backed problem

Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features

Evidence-backed problem

A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study

Evidence-backed gain

Can Artificial Intelligence Match Human Expertise in Long-Term Periodontal Prognosis? A Comparative Accuracy Study

To compare the prognostic performance of an artificial intelligence (AI) model with that of experienced clinicians in predicting tooth loss over a 10-year period. An AI model trained on structured clinical and radiographic data was compared with 12 periodontists and 11 general dentists (GDs), who independently assigned prognostic scores (0-10 scale) to 300 teeth with known 10-year outcomes. AI and clinician performance were evaluated under a fixed threshold and group-specific optimal thresholds. Accuracy, sensit…

$Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial$

Evidence-backed problem

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial

Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited. We will conduct a prospective cluster randomised cro…

Evidence-backed problem

Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency

To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without…

Evidence-backed problem

Simulating Reality and Transforming Endoscopic Education-Lessons from Esophageal Dilation

Although endoscopic training has traditionally relied on mentorship and procedural volume to assess proficiency, the major gastroenterology societies have increasingly adopted competency-based assessment tools, including the Assessment of Competency in Endoscopy (ACE) and Direct Observation of Procedural Skills (DOPS), to provide a more objective evaluation of technical and cognitive skills. Despite these advances, mentor-based assessment remains susceptible to subjectivity. Emerging technologies such as simulat…

Health

Multidisciplinary dental treatment planning by artificial intelligence: A comparative evaluation

Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt…

Science

Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs

Estimating a biological profile, such as sex, is a fundamental step in forensic identification when primary identifiers are unavailable for direct individual comparison. In forensic scenarios involving advanced decay, specific taphonomic alterations, or midfacial blunt force impacts, the mandibular ramus serves as a valuable anatomical marker due to its distinct sexual dimorphism and thick cortical structure, making it more resilient to fragmentation than other, more fragile facial bones. Despite its utility, th…

Health

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

Background Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need. However, existing artificial intelligence models often function as black boxes and are trained on single, pre-selected images, which does not reflect the clinical workflow where multiple images are assessed. Methods We con…

Labor

Technology-Enabled Interventions for Preventing and Responding to Elder Abuse: A Review of the Current Evidence

This scoping review consolidates existing technology-enabled interventions for preventing and responding to abuse of older people (AOP). Studies reporting original, evaluated interventions in which technology delivered over 50% of the content or enabled coordination were included. Ten primary databases, four gray literature sources, and two artificial intelligence‑assisted platforms were searched for articles published between January 1, 2013 and December 31, 2024. Two reviewers independently assessed study qual…

Health

Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Background Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation. Methods A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-sys…

Health

Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs…

Health

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

Background The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy is reduced in such patients due to low albumin levels. The albumin-corrected anion gap (ACAG) enhances the accuracy of assessing acid-base imbalances. Individuals with traumatic lung injury (TLI) in the intensive care unit (ICU) often have severe metabolic acidosis and hypoalbuminemia. Nevertheless, the association of ACAG with the prognosis of patients with TLI is…