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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Mapping artificial intelligence integration in objective structured clinical examinations: A scoping review

INTRODUCTION: Objective Structured Clinical Examinations (OSCEs) are widely used to assess clinical competence, but face challenges related to examiner workload, scoring variability, delayed feedback, and resource demands. Although AI may address these constraints and support precision medical education, the evidence remains fragmented. This scoping review maps AI applications in OSCEs. METHODS: We followed PRISMA-ScR. We searched MEDLINE, Scopus, Embase, Web of Science, ERIC, LILACS, and IEEE Xplore from incept…

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Mapping artificial intelligence integration in objective structured clinical examinations: A scoping review

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Combining pathology artificial intelligence and genomic biomarkers to refine long-term postprostatectomy outcome prediction

BACKGROUND: A multimodal AI (MMAI) model has been validated in prostate biopsy specimens to guide treatment intensification in men receiving radiation. The MMAI has been explored to an extent for prostatectomy patients and has not yet been examined in relation to established genomic scores. METHODS: We applied the MMAI biopsy model to a tissue microarray (TMA) of 424 prostatectomy cases with long-term follow-up. MMAI scores were derived from digitized pathology images and clinical variables. Associations with bi…

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Improving turnaround times with artificial intelligence in microbiology

This dual-center study evaluated the impact of artificial intelligence (AI) on urine culture turnaround times in Canadian diagnostic laboratories using microbiology laboratory automation. Data were collected before and after the implementation of PhenoMATRIX (PM), an AI-based software that provides continuous culture sorting and result interpretation support. In both a low-volume tertiary care hospital and a high-volume community laboratory, PM enabled earlier availability of interpretable results; however, redu…

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Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis

Background: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demonstrate promise by targeting bone-remodeling pathways, yet evidence for their efficacy and safety remains fragmented and heterogeneous, and no prior systematic review in OI has incorporated artificial intelligence (AI) to synthesize it. Objective: This study aims to systematically evaluate the efficacy and safety of novel biologics in patients with OI using an AI-ass…

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Beyond EuroSCORE II: is artificial intelligence ready to redefine risk stratification in cardiothoracic surgery?

Risk stratification is central to contemporary cardiothoracic surgical practice, guiding patient selection, perioperative planning, informed consent, and benchmarking of outcomes across institutions. Established models such as European System for Cardiac Operative Risk Evaluation II and the Society of Thoracic Surgeons risk score remain widely used because they are validated, interpretable, and embedded within routine clinical workflows. However, their static structure and reliance on predefined variables may li…

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Model-prefilled problem

Beyond the algorithm: health technology assessment frameworks for AI in cardiology under the European Union Health Technology Assessment Regulation: a systematic review

Background: Artificial intelligence (AI) is increasingly used in cardiovascular care to support diagnosis, monitoring and clinical decision-making. However, its dynamic and adaptive nature challenges conventional health technology assessment (HTA) frameworks, which are typically designed for static interventions. This review aims to assess how existing literature supports HTA-relevant evaluation of AI-based cardiovascular technologies and examine their alignment with the evidentiary requirements outlined in the…

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Triage safety of patient-facing AI chatbots for nipple discharge: A guideline-informed assessment of red-flag recognition and patient actionability

Objective To evaluate red-flag recognition, clinical safety, and the quality of patient actionability in responses generated by artificial intelligence (AI) chatbots to patient questions about nipple discharge. Methods This guideline-informed cross-sectional evaluation was conducted to assess the performance of AI chatbots in simulated nipple discharge consultations. A total of 36 English-language simulated patient questions were developed on the basis of clinical guidelines and real-world consultation scenarios…

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Quantifying the impact of slice thickness on cardiovascular risk stratification in lung cancer screening: a multi-center "RESCUE" study

Background: Patients undergoing routine non-gated chest computed tomography (CT) for health checkups or atypical chest discomfort often present with a coronary artery calcium (CAC) score of zero on standard 5.0 mm reconstructions. We hypothesized that these thick slices obscure mild calcification due to partial volume effects (PVEs), which could be recovered by retrospective analysis of native thin-slice images. This study aimed to quantify the rate of unrecognized coronary calcification on standard thick-slice…

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Quantifying the impact of slice thickness on cardiovascular risk stratification in lung cancer screening: a multi-center "RESCUE" study

Embedded transparency in artificial intelligence: a prerequisite for equity and representation in AI-enabled clinical trials

Artificial intelligence is being embedded in clinical trial infrastructure, shaping who is identified, stratified, and analysed. Opaque models risk amplifying existing disparities in the evidence base. We argue that embedded transparency, the structural integration of ex ante interpretability, demographic auditability, documented uncertainty handling, and stakeholder-relative explanation, is a necessary, though not sufficient, condition for equitable AI-enabled trials, and propose governance recommendations acti…

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Embedded transparency in artificial intelligence: a prerequisite for equity and representation in AI-enabled clinical trials

Performance evaluation of domain-specific and general-purpose AI models for chest radiograph interpretation: a comparative study

Chest radiography remains the most widely used imaging modality worldwide; however, its interpretation is inherently challenging because of overlapping anatomical structures and subtle findings. Recent advances in multimodal large language models (LLMs) have enabled automated radiology report generation, yet their clinical performance relative to domain-specific medical AI systems remains insufficiently validated. This study aimed to evaluate the performance and clinical applicability of a domain-specific multim…

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Performance evaluation of domain-specific and general-purpose AI models for chest radiograph interpretation: a comparative study

Seeing beyond the algorithm: artificial intelligence and the enduring role of the radiologist

Artificial intelligence (AI) has rapidly emerged as a transformative force in radiology, offering enhanced diagnostic accuracy, workflow optimization, and the potential to alleviate rising imaging demands. As radiology remains inherently dependent on pattern recognition and high-volume data interpretation, it represents an ideal domain for AI integration. This narrative review synthesizes current evidence on the clinical impact of AI across multiple dimensions of radiologic practice, including diagnostic perform…

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Seeing beyond the algorithm: artificial intelligence and the enduring role of the radiologist

Adopting AI Enhances Humanitarian Operations While Demanding Critical Trade-Offs

Artificial intelligence (AI) is increasingly impacting the cooperation sector, transforming how humanitarians are operating in the field. AI tools now allow for improved health diagnostics and quality services. In conflict areas, organizations are able to improve the efficiency of their analysis, and propose optimized data management processes for more multifactorial predictions. In a context where human and financial resources are limited, AI address the gaps to sustain emergency responses. In this commentary,…

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Adopting AI Enhances Humanitarian Operations While Demanding Critical Trade-Offs

A Nurse Hackathon: Improving Effective and Timely Nurse Handoffs Through Use of Generative Artificial Intelligence

Effective handoff communication between the emergency department and inpatient units is essential for patient safety and nurse well-being. This project uses an innovative design strategy and generative artificial intelligence to improve the quality and consistency of information exchanged during nurse-to-nurse handoffs. By generating computer-based summaries, key patient details can be accurately conveyed, reducing the need for manual review and customization. Developed through a collaborative hackathon with fro…

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A Nurse Hackathon: Improving Effective and Timely Nurse Handoffs Through Use of Generative Artificial Intelligence

Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal

Background: Systematic reviews are essential for evidence-based practice but remain resource-intensive, particularly during full-text data extraction and structured risk-of-bias appraisal in prognostic research. These challenges are amplified in complex autoimmune diseases such as systemic lupus erythematosus (SLE). Recent advances in large language models (LLMs) have raised interest in their potential; however, rigorous benchmarking against expert reviewers in real-world rheumatology settings is limited. Object…

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Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal

Artificial Intelligence-Based CTA Software for Real-World Detection of Large Vessel Occlusion in Acute Ischemic Stroke

Background and purpose Artificial intelligence (AI)-based tools for CT angiography (CTA) have been introduced to support rapid detection of large vessel occlusion (LVO) in acute ischemic stroke. Although regulatory approval has relied mainly on controlled clinical studies, evidence from routine clinical practice remains limited. This study aimed to assess the real-world diagnostic performance and workflow impact of the Brainomix e-CTA software. Materials and methods We conducted a retrospective, single-center ob…

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Artificial Intelligence-Based CTA Software for Real-World Detection of Large Vessel Occlusion in Acute Ischemic Stroke