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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

339 stories · page 19 of 23

Model-prefilled gain

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study

Background Large symptomatic brain metastases require initial surgical resection. However, local control (LC) after Gamma Knife radiosurgery (GKRS) to resection cavities remains variable. Quantitative risk stratification using routinely available treatment-time variables could inform surveillance and multidisciplinary decision-making. Methods We performed a retrospective study of post-resection cavities treated with GKRS at a single institution (2014-2024). The primary endpoint was LC. The cohort comprised of 40…

Journal of Neuro-Oncology · Health

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study

Model-prefilled gain

Impact of Artificial Intelligence-Enhanced Insertable Cardiac Monitors on Device Clinic Workflow and Resource Utilization

BACKGROUND: Insertable cardiac monitors (ICMs) are essential for managing arrhythmias but often generate large numbers of transmissions and false alerts. Integrating artificial intelligence (AI) as part of the ICM workflow can reduce this burden. However, its impact on clinic workflow and resource utilization must be better understood. OBJECTIVES: The aim of the study was to assess the impact of AI-enhanced ICMs on clinic workflow and resource utilization. METHODS: A cross-sectional analysis was conducted using…

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Model-prefilled gain

From severity scoring to predictive analytics: the emerging role of AI in neurosurgery

The rapid integration of artificial intelligence (AI) into neurosurgical practice is transforming every phase of patient care from diagnostic imaging and preoperative planning to intraoperative decision-making and postoperative management. This narrative review traces the evolution of data-driven neurosurgery, beginning with traditional severity scoring systems and advancing toward predictive analytics and intelligent automation. By examining structured data (such as electronic health records and laboratory valu…

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Model-prefilled gain

An analysis of the real world performance of an artificial intelligence based autism diagnostic

Rapidly rising demand for pediatric autism evaluations has outpaced specialist capacity and created a crisis of delayed diagnoses and treatment. Streamlining the diagnostic process could reduce wait times and optimize use of limited specialist resources. Following strong clinical trial results, Canvas Dx, an AI-based diagnostic, was FDA authorized to support accurate diagnosis or rule-out of autism in children 18-72 months with caregiver or healthcare provider concern for developmental delay. To gain insight int…

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Both readings

Reproducibility and Validation Challenges in AI-Driven Scaffold Design for Bone Regeneration

Artificial intelligence (AI) is increasingly applied to bioink formulation and bioprinting process control in tissue engineering (TE). Yet, translational progress remains constrained by small datasets, limited cross-platform validation, and weak links between computational predictions and biological outcomes. This review critically evaluates experimentally validated AI applications across scaffold-based bone regeneration, spanning materials design, fabrication control, and biological assessment. Physics-informed…

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$A CT-based deep learning model for the automated risk stratification of refractory Mycoplasma pneumoniae pneumonia in children$

Evidence-backed gain

A CT-based deep learning model for the automated risk stratification of refractory Mycoplasma pneumoniae pneumonia in children

The accurate identification of children with refractory Mycoplasma pneumoniae pneumonia (RMPP) remains challenging. This study aimed to develop a transformer-based model utilizing clinically indicated chest computed tomography (CT) to stratify pediatric RMPP risk at a critical decision point. Non-contrast chest CT data from a multicenter retrospective cohort of 1224 pediatric patients with Mycoplasma pneumoniae pneumonia who underwent clinically indicated CT were used to develop a transformer-based deep learning…

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Evidence-backed problem

Bias in medical AI: Implications for clinical decision-making

Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences, especially in applications that involve clinical decision-making. Left unaddressed, biased medical AI can lead to substandard clinical decisions and the perpetuation and exacerbation of longstanding healthcare disparities. We discuss potential biases that can arise at different stages in the AI development pipeline and how they can affect AI algorithms and clinic…

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Digital Twins’ Advancements and Applications in Healthcare, Towards Precision Medicine

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental inf…

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Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Resea…

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Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However,…

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Davinci xi: FDA injury report involving AI

The FDA received a injury report involving Davinci xi, made by Intuitive surgical, inc. A review of a clinical literature article titled "usefulness of artificial intelligence for surgical support in robot-assisted distal pancreatectomy: a preliminary case report" was conducted. the article reported an event involving a da vinci-assisted distal pancreatectomy where the dissection line on the lower margin of the pancreas was falsely recognized and pancreatic tissue was damaged. the estimated blood loss…

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Prediction of bronchopulmonary dysplasia seven days after birth using respiratory and oxygenation timeseries with machine learning

Accurate prediction of bronchopulmonary dysplasia (BPD) development would allow targeted early treatment. This study aims to develop a machine learning (ML) model incorporating respiratory and oxygenation timeseries to predict BPD development within 1 week after birth. Data was collected retrospectively from a neonatal intensive care (2009-2015). Readily available clinical data and respiratory and oxygenation timeseries (mode of respiratory support, FiO2, SpO2) were gathered. Descriptive features were extracted…

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Clinical applications of artificial intelligence in hypertension management: current evidence and future perspectives

Hypertension remains the leading modifiable risk factor for cardiovascular morbidity and mortality worldwide, with persistently inadequate blood pressure control despite guideline-directed therapy. The rapid expansion of digital health data and computational capacity has positioned artificial intelligence (AI) as a promising tool for improving hypertension management through enhanced risk prediction, phenotyping, and individualized care. However, important challenges related to external validation, interpretabil…

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Clinical applications of artificial intelligence in hypertension management: current evidence and future perspectives

Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions

Ischemic stroke management is time-sensitive, and lesion segmentation supports treatment selection, prognostication, and reproducible quantification. Deep learning (DL) aims to accelerate and standardize lesion delineation to augment neuroimaging workflows. We conducted a narrative review of DL-based ischemic stroke lesion segmentation studies published from 2020 to 2025. PubMed, Google Scholar, Scopus, and IEEE Xplore were searched; ~ 500 records were identified, and 40 full-text studies were included after scr…

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Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions

Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department

Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (ED). Standardized clinical scoring systems, including the Wells and revised Geneva scores, form the cornerstone of initial risk stratification but have limited specificity, leading to unnecessary D‑dimer testing and frequent overuse of CT pulmonary angiography (CTPA). This study aimed to develop explainable machine‑learning (XML) models as a complementary decision‑support laye…

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