Surveillance

Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices.

The integration of artificial intelligence (AI) and machine learning (ML) into medical devices has revolutionized healthcare, enhancing diagnostic accuracy and clinical decision-making. However, their rapid evolution poses challenges to traditional regulatory frameworks, particularly in ensuring safety and effectiveness. This review examines current Food and Drug Administration (FDA) regulatory pa...

Machine learning in geriatric care: a scoping review of models using multidimensional assessment data.

BACKGROUND: Geriatric assessments capture multidimensional data on physical, cognitive, psychological, and social health, offering opportunities to apply machine learning (ML) to support clinical decision-making in aged care. However, the application of ML to such data has not been systematically synthesised. OBJECTIVE: To describe the types of data used, purposes, and performance of ML models app...

Artificial intelligence in spine care: A scoping review of treatment applications.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in healthcare to support decision-making, personalize treatment, and improve outcomes...

Feasibility of deep learning-accelerated HASTE-FS for pancreatic cystic lesion surveillance: comparison with conventional HASTE and MRCP.

PURPOSE: Pancreatic cystic lesions (PCL) commonly undergo surveillance using MRI with MR cholangiopancreatography (MRCP). Our objective is to compare ...

Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.

Oropharyngeal cancer (OPC) is increasingly driven by human papillomavirus (HPV), particularly HPV16, marking a shift in its epidemiology, prognosis, a...

Utility and limitations of the FDA adverse events reporting system public dashboard for safety analyses: a case study with vesicular monoamine transporter 2 inhibitors.

INTRODUCTION: The United States Food and Drug Administration (FDA) requires post-marketing surveillance of approved drugs, and pharmaceutical manufact...

Machine learning models for predicting hospital admission in pediatric emergency departments: A systematic review.

CONTEXT: Pediatric Emergency Departments (PEDs) face overcrowding partially due to delayed hospital admission decision. Machine Learning (ML) models c...

Retinal Age Gap as a Biomarker for Patient Satisfaction and Visual Quality After Cataract Surgery.

PURPOSE: To evaluate whether the difference between artificial intelligence (AI)-estimated retinal biological age and chronological age-the retinal ag...

Predictive surveillance and diagnosis of COVID-19: An integrative machine learning and wastewater multi-omics approach.

COVID-19 has had major global impacts, highlighting the importance of robust predictive surveillance and diagnostic systems to ensure effective public...

From wastewater to epidemiological insights: A systematic review of modeling strategies for infectious disease surveillance.

Wastewater-based epidemiology (WBE) has emerged as a promising complementary tool in infectious diseases surveillance systems, offering real-time insi...

Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.

OBJECTIVE: Develop a deep learning model for automatic hepatocellular carcinoma (HCC) detection in T1 weighted imaging (WI) Dynamic Contrast-Enhanced ...

Risk prediction models for adverse drug reactions in pediatrics: a scoping review.

INTRODUCTION: Predictive models play a critical role in enhancing medication safety in clinical practice. While multiple models for adverse drug react...

Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.

Chronic kidney disease (CKD) represents a major and expanding global health challenge, with prevalence rising due to aging populations, diabetes, hype...

Zoonoticus: A machine learning model for genomic prediction of zoonotic bacterial strains using virulence, resistance, and mobile genetic elements.

Zoonotic diseases continue to rise globally, yet no existing genomic tool integrates virulence, antimicrobial resistance (AMR), and mobile genetic ele...

An Internet of Things operating room platform for neurosurgery.

OBJECTIVE: The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overco...

Letter to the editor: "How do experts classify sepsis cases for sepsis surveillance? Lessons learned from a behavioural artificial intelligence technology (BAIT) approach".

Behavioural Artificial Intelligence Technology (BAIT) has recently been proposed to codify expert reasoning for sepsis surveillance. We provide prelim...

Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database.

OBJECTIVE: Maintaining robust surveillance programs for abdominal aortic aneurysms (AAAs) is important, but these programs are expensive and labor-int...

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine lea...

Co-Designing and Evaluating a Digital Competencies Toolkit for Nursing Students.

AIM: To offer a student-focused critical evaluation of the content and use of a digital competencies discipline-specific toolkit that was co-designed ...

Machine learning in colorectal cancer prediction and diagnosis: a systematic review of models' performance.

INTRODUCTION: Colorectal cancer (CRC) poses a significant global health burden, demanding early and accurate detection strategies. However, Machine Le...