This paper presents insights from the work of the Canadian Community of Practice in Ecosystem Approaches to Health (CoPEH-Canada) and 15 years (2008-2022) of land-based, transdisciplinary, learner-centred, transformative learning and training. We have oriented our learning approaches to Head, Hands, and Heart, which symbolise cognitive, psychomotor, and affective learning, respectively. Psychomotor and affective learning are necessary to grapple with and enact far-reaching structural changes (eg, decolonisation) needed to rekindle healthier, reciprocal relationships with nature and each other. We acknowledge that these approaches have been long understood by Indigenous colleagues and communities. We have developed a suite of teaching techniques and resources through an iterative and evolving pedagogy based on participatory approaches and operating reciprocal, research-pedagogical cycles; integrated different approaches and ways of knowing into our pedagogy; and built a networked Community of Practice for continued learning. Planetary health has become a dominant framing for health-ecosystem interactions. This Viewpoint underscores the depth of existing scholarship, collaboration, and pedagogical expertise in ecohealth teaching and learning that can inform planetary health education approaches.
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http://dx.doi.org/10.1016/S2542-5196(22)00305-9 | DOI Listing |
J Clin Med
December 2024
Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA.
The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Conservative Dentistry & Endodontics, Narayana Dental College and Hospital, Nellore 523004, Andhra Pradesh, India.
Artificial intelligence (AI) is an area of computer science that focuses on designing machines or systems that can perform operations that would typically need human intelligence. AI is a rapidly developing technology that has grabbed the interest of researchers from all across the globe in the healthcare industry. Advancements in machine learning and data analysis have revolutionized oral health diagnosis, treatment, and management, making it a transformative force in healthcare, particularly in dentistry.
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December 2024
School of Innovation, Design and Engineering, Division of Intelligent Future Technologies, Mälardalens University, 721 23 Västerås, Sweden.
Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions.
View Article and Find Full Text PDFBiomedicines
November 2024
Bio and Emerging Technology Institute (BETin), Addis Ababa P.O. Box 5954, Ethiopia.
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.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.
Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR).
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