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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging

Artificial intelligence (AI) has rapidly transformed cardiac CT, extending its clinical utility from coronary CT angiography (CCTA) to CT myocardial perfusion imaging (CT-MPI). This review outlines the current advances in and future perspectives on AI-aided cardiac CT across anatomical, functional, and prognostic dimensions. In CCTA, AI can automate calcium scoring, vessel segmentation, and plaque characterization, markedly improving workflow efficiency and reproducibility. Deep-learning models can allow accurat…

Korean Journal of Radiology · Health

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A polyline searching-driven evolutionary AI for disease detection of medical imaging data

Objective. Accurate Ultrasound (US) prostate cancer (PCa) segmentation images hold significant value for organ interventional guidance and clinical disease diagnosis. However, this task still poses substantial challenges. The main obstacles include blurred or incomplete boundaries separating PCa from adjacent soft tissues, shadow artifacts inherent to ultrasound imaging, and drastic inter-patient variations in organ morphological shapes. Approach . To address these issues, our method introduces a novel coarse-to…

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Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis

Objective We set out to examine how completely artificial intelligence (AI)-based orthopedic studies report their methods. Methods A PubMed search covering 2023-2025 was run with a predefined strategy. Screening of titles, abstracts and full texts left 280 eligible papers, and 200 of these were drawn at random for scoring. Each paper was rated against a six-item framework built from the TRIPOD-AI, STARD-AI and CONSORT-AI guidelines. Results Mean score across the 200 studies was 4.8±0.9. Twenty-eight percent reac…

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Evidence-backed problem

Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations

Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post-traumatic stress disorder-despite evidence that a range of psychiatric disorders may follow trauma. We applied machine learning to identify key post-traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population-based cohort. Methods We conducted a case-control study of…

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Evidence-backed problem

A strategic pathway for the ethical development of AI tools in dementia care

Artificial intelligence (AI) is rapidly entering dementia clinical practice, offering opportunities across the care continuum. However, cognitive decline creates a unique ethical challenge. This perspective article proposes a strategy to ensure the responsible development and deployment of AI tools for dementia. An interdisciplinary workgroup of the Alzheimer's Association Innovation Roundtable synthesized ethical frameworks, regulatory standards, and empirical evidence to generate expert consensus. The result i…

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Evidence-backed gain

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study

Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use. This study therefore proposes a highly sensitive artificial intelligence (AI)-assisted diagnostic system for GAS based exclusively on H&E-stained histopathological images. We incl…

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Both readings

Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis

Background Artificial intelligence (AI) methods are increasingly used to strengthen policy evaluation in managed care pharmacy. Among Medicare beneficiaries with cancer, which is one of the most clinically complex and costly populations, prescription drug coverage is obtained through either integrated Medicare Advantage Prescription Drug plans (MA-PDs) or stand-alone Prescription Drug Plans (PDPs). However, causal evidence of plans' impact remains limited because of nonrandom enrollment. Objective To apply an AI…

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An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications

Background Artificial intelligence (AI) applications in managed care pharmacy depend on semantically consistent medication data, yet heterogeneous medication identifiers across real-world electronic health records (EHRs) could undermine analytic fidelity and risk propagating classification errors. To enable transportable, reproducible AI tools, methods for harmonizing disparate medication identifiers (eg, National Drug Code [NDC] and Multum drug synonym ID) to standardized vocabularies are required. Objective To…

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A pharmacist-overseen, artificial intelligence-enabled model for provider-side prior authorization: From burden to opportunity (PAVE-1)

Prior authorization (PA) imposes substantial administrative burdens on clinicians, contributing to burnout, delayed care, and excess health care spending. Artificial intelligence (AI) is emerging as a tool to automate PA tasks in managed care pharmacy, yet concerns persist regarding transparency, bias, and overreliance on autonomous systems. This is particularly true in payer-deployed AI systems that may deny claims without adequate clinical review. This viewpoint proposes a pharmacist-overseen, AI-enabled syste…

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A pharmacist-overseen, artificial intelligence-enabled model for provider-side prior authorization: From burden to opportunity (PAVE-1)

[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]

Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardio…

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[Breast arterial calcifications and cardiovascular risk in women]

Cardiovascular (CV) diseases remain the leading cause of mortality in women and often present as the first clinical manifestation, highlighting the limitations of current risk scores. In this context, breast arterial calcifications (BACs), incidentally detected on screening mammography, are emerging as a potential biomarker of systemic CV risk. This narrative review summarizes the most recent evidence (2023-2026) on the association between BACs and CV outcomes, including observational studies, prospective and re…

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Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges

Xenotransplantation represents a potential solution to the persistent global organ shortage, yet its clinical application remains stalled by complex immunologic responses, coagulation dysregulation, species-specific biology, and infectious risks. Artificial intelligence (AI) could enhance safety, accelerate decision-making, and enable precision medicine initiatives within this rapidly evolving field. However, effective implementation of AI in xenotransplantation requires approaches specifically adapted to the bi…

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Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges

Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study

Aim To compare the multidimensional performance of discharge instructions generated by generative AI (GPT-4) versus those created by clinical registered nurses across three dimensions-accuracy, empathy and readability-and to explore the impact of patient. Design A prospective, double-blind, vignette-based cross-sectional study. Methods Five standardized multidisciplinary discharge scenarios were constructed. Discharge instructions were generated independently by five registered nurses and GPT-4. Fifteen clinical…

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Multisite Implementation of a Digital Wound Model of Care: A Post-Implementation Multimethods Evaluation of Patient and Clinician Perspectives and Lessons Learned

This study evaluated district-wide implementation of a digital wound model of care combining an artificial intelligence-enabled application with a virtual command centre across four hospitals and five community health centres in Australia. A post-implementation multimethods evaluation (January 2024-January 2026) of patients (n = 94), frontline clinicians (n = 75), and senior wound nurses (n = 9) and a product manager (n = 1) using surveys, semi-structured interviews and analysis of governance meeting minutes. Pa…

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A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL

Abstract Body composition analysis (BCA) provides an objective assessment of metabolic states, but its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains unclear. We applied machine learning-supported BCA to computed tomography imaging from patients with newly diagnosed DLBCL enrolled in the prospective phase 3 PETAL trial to quantify radiologic sarcopenia. We assessed BCA results in relation to survival after first-line immunochemotherapy, treatment-related hematologic toxicities, and molecular d…

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