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626 stories · page 14 of 42

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How Well Do AI Chatbots Understand Abnormal Anatomy: A Comparative Study Using Congenital Anomalies and Tumor Cases

Artificial Intelligence (AI) chatbots are becoming an efficient option to understand medical data and also help with clinical reasoning. There has been a recent progression in research of large language models and their ability to be used in the healthcare sector, such as radiological image analysis, and diagnostic support. There is however, little evidence supporting their ability to accurately understand abnormal anatomical conditions, such as congenital anomalies and tumor related changes in anatomy. To asses…

Clinical Anatomy

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Stakeholder perspectives on artificial intelligence in schizophrenia care

Evidence-backed gain

Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review

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Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study

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Beyond the Algorithm: A Stewardship Framework for the Hand Surgeon Adopting Artificial Intelligence

Artificial intelligence is entering hand surgery through imaging, outcome prediction, and patient communication. Neural networks read scaphoid and distal radius radiographs. Machine learning models predict outcomes after carpal tunnel release. Large language models are being tested for patient communication and chart drafting. Adoption, however, has outpaced validation. Most hand surgery artificial intelligence tools are tested only on data resembling their training set, deployed in workflows that have not been…

Evidence-backed problem

Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-<i>AJR</i> Expert Panel Review

Artificial intelligence (AI) applications have transformed radiology, yet pediatric medical imaging remains substantially underrepresented in AI development, validation, regulation, and implementation. Unlike adults, children go through continuous physiologic and anatomic changes that require age-specific models trained on representative developmental data. However, pediatric AI is limited by scarce publicly available datasets, fragmented institutional data, rare diseases, heterogeneous reporting practices, and…

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A Quality Assessment Rubric for Artificial Intelligence-Generated Patient-Friendly Radiology Reports

Background: Artificial intelligence (AI) tools are being used to translate radiology reports into plain language, but translation errors may compromise comprehension and safety. Objective: To develop and evaluate a rubric for assessing the quality and safety of AI-generated patient-friendly radiology reports. Methods: In this prospective study (conducted from February 2025 to December 2025), survey-workshop cycles, involving lay participants and a multidisciplinary panel, were used to develop a rubric for gradin…

Evidence-backed problem

AI Is Rewriting Biology And Medicine, But Human Oversight Remains Crucial

AI is increasingly being used to design genetic instructions and analyse medical data, opening new possibilities in biology and healthcare. Researchers have created functioning virus designs using AI, while other systems can detect hidden signals in ECGs and identify patients at risk. Experts say human oversight remains essential as AI capabilities grow.

Health

Machines that express emotions: from medical paternalism to machine paternalism?

Abstract: Introduction: Machines do not have emotions, but they can be programmed to express (to appear to have) certain emotions. In medicine, robots and AI machines are used that could express positive emotions (joy, compassion, hope, gratitude, inspiration or awe) and priorities. Objective: To describe the main ethical implications of integrating the expression of emotions in machines used f...

Health

Causal adiposity and clinical validation of regional fat distribution in PMOS: a multi-modal analysis integrating GBD 2021, Mendelian randomization, and machine learning

Background Polyendocrine metabolic ovarian syndrome (PMOS), previously named polycystic ovary syndrome (PCOS), is the most common endocrine disorder among women of reproductive age and a leading cause of anovulatory infertility. However, the evolving global burden of PMOS and the role of adiposity, particularly regional fat distribution, remain incompletely understood. We integrated global epidemiological analysis, genetic causal inference, and clinical prediction modeling to investigate the burden and adiposity…

Health

A protocol for validation of novel artificial intelligence-based framework for dyspnoea investigation with cardiopulmonary exercise testing

Background Exertional dyspnoea largely represents the sensory translation of an ever-growing dynamic mismatch between ventilatory demand and capacity as exercise intensifies. This fundamental tenet, however, has not been formally incorporated into data display and clinical interpretation of incremental cardiopulmonary exercise testing (CPET). The objectives of the present study were to validate a novel framework (Dynamic Assessment of Dyspnoea and Ventilation on Exercise (DyVe-X)) to quantify the severity of exe…

Health

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and inconsistencies in existing diagnostic tools. This review evaluates the role of machine learning and deep learning approaches in improving ASD prediction, with a particular focus on two important yet r…

Health

Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model

Background To develop an artificial intelligence (AI)-assisted model for detecting skull fractures in neonates and infants using plain radiographs, enhancing diagnostic accuracy while minimizing radiation exposure. Methods A retrospective dataset of skull X-rays from 1,184 patients with head trauma (2010-2021) was collected. Images underwent preprocessing, including background removal, Gaussian blurring, binarization, and CLAHE-based contrast enhancement. Three convolutional neural network architectures (ResNet-…

Health

The AI arc and interpretive drift

Artificial intelligence now enters the clinical encounter at three points: before the visit, during clinical reasoning, and after it, when ambient tools generate the note. AI has the potential to adversely influence the diagnostic process at each of these steps. This opinion piece names interpretive drift, the subtle shift in meaning that occurs as a patient's account moves through an AI-generated summary and into the medical record. It argues that reviewing AI-generated documentation is a diagnostic safety prac…

Health

Species-level identification of Nocardia spp. from clinical samples via intelligent analysis of Raman spectroscopic fingerprints

Background Nocardia spp. are clinically opportunistic pathogens that are frequently underdiagnosed. They often lead to severe clinical consequences. These infections are often invasive, involving the lungs, nervous system, skin, and soft tissues. Different Nocardia spp. show significant differences in virulence and antimicrobial susceptibility. However, clinical manifestations are highly diverse, and species-level identification remains technically difficult. The precise diagnosis of Nocardia spp. is challenging…