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Empowering healthcare professionals with no-code artificial intelligence platforms for model development, a practical demonstration for pathology.
Discoveries (Craiova)
March 2024
Department of Pathology, Westchester Medical Center, 100 Woods Rd, Valhalla, NY 10595, USA.
Artificial intelligence (AI) and machine learning based applications are thought to impact the practice of healthcare by transforming diagnostic patient management approaches. However, domain knowledge, clinical and coding expertise are likely the biggest challenge and a substantial barrier in developing practical AI models. Most informatics and AI experts are not familiar with the nuances in medicine, and most doctors are not efficient coders.
View Article and Find Full Text PDFExamining Patient Safety Events Using the Behaviour Change Wheel: A Cross-Sectional Analysis.
Jt Comm J Qual Patient Saf
November 2024
Background: Precursor-level safety events (PSEs) pose greater patient risk than no-harm events but are not as severe as serious safety events. Despite their potential for harm, the underlying determinants associated with PSEs are poorly understood. This study aimed to use a behavior change framework to understand the underlying determinants of PSEs and whether associated action items aligned with the behavior.
View Article and Find Full Text PDFArtificial intelligence after the bedside: co-design of AI-based clinical informatics workflows to routinely analyse patient-reported experience measures in hospitals.
BMJ Health Care Inform
December 2024
Queensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Saint Lucia, Queensland, Australia.
Objective: To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.
Methods: The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows.
Teaching design students machine learning to enhance motivation for learning computational thinking skills.
Acta Psychol (Amst)
November 2024
Department of Computational Media, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. Electronic address:
The integration of computational thinking (CT) to enhance creativity in design students has often been underexplored in design education. While design thinking has traditionally been the cornerstone of university design pedagogy and remains essential, the increasing role of digital tools and artificial intelligence in modern design practices presents new opportunities for innovation. By introducing CT alongside design thinking, students can expand their creative toolkit and engage with emerging technologies more effectively.
View Article and Find Full Text PDFcidalsDB: an AI-empowered platform for anti-pathogen therapeutics research.
J Cheminform
November 2024
Laboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, 13, Place Pasteur, 1002, Tunis, Tunisia.
Computer-aided drug discovery (CADD) is nurtured by late advances in big data analytics and Artificial Intelligence (AI) towards enhanced drug discovery (DD) outcomes. In this context, reliable datasets are of utmost importance. We herein present CidalsDB a novel web server for AI-assisted DD against infectious pathogens, namely Leishmania parasites and Coronaviruses.
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