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Development of a Preliminary Patient Safety Classification System for Generative AI.
BMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
View Article and Find Full Text PDFApplying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.
J Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
View Article and Find Full Text PDFBackground: Alzheimer's disease (AD) is heterogeneous in both its clinical and neuropathologic course. Age at onset and distribution of corticolimbic tangles can vary widely among individuals. Genetic risk factors APOE ε4 and MAPT H1 increase AD risk.
View Article and Find Full Text PDFBasic Science and Pathogenesis.
Alzheimers Dement
December 2024
Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Background: To gain a deeper understanding of underlying molecular mechanisms in genomic regions associated with Alzheimer's disease (AD), the National Institute on Aging (NIA) launched the Alzheimer's Disease Sequencing Project (ADSP) Functional Genomics Consortium (FunGen-AD) in 2021.
Method: The first effort of this collaboration, coordinated by the NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS), aggregated functional genomics (FG) data from 5 cohorts, including ∼3,000 samples of European (EA) and African ancestries (AA). We used this data to map Quantitative Trait Loci (xQTL) on AD-specific human tissues and cells, providing insights into how non-coding genetic variants contribute to AD risk.
Clinical Manifestations.
Alzheimers Dement
December 2024
Imperial College London, Department of Brain Sciences, London, United Kingdom.
Background: Cognitive assessments are essential for detecting and monitoring cognitive changes in neurological populations. Compared to standard pen-and-paper tests, online cognitive tasks offer a more accessible, scalable, repeatable and cost-effective approach to assessment. Cognitron is an online cognitive assessment platform with previously demonstrated validity and reliability (1).
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