Encyclopedia

Medical logistics often face constraints of distance, terrain, and damaged infrastructure, necessitating the evaluation of new technologies, such as Uncrewed Aerial Vehicles (UAVs) or drones, as solutions. However, translation of this technology into the clinical space requires not only proof of time efficiency but also the ability of the payload to reach its destination with preserved viability—maintaining the stability, sterility, and potency of the medical supplies after transport. The authors conducted a systematic search of nine databases from 2010 to 2026, surfacing 38 peer-reviewed studies examining UAV transport of medical devices, pharmaceuticals, and biological specimens including blood products. Viability of payloads was maintained in the majority of trials. No significant differences comparable with ground transport were shown with pharmaceuticals. All medical devices remained clinically usable, and a majority of biological samples maintained their viability. The existing body of literature consists primarily of small studies conducted in real, but still favourable operating conditions, with a mix of flight distances and visual or beyond line-of-sight rules. These studies report substantial heterogeneity in methodology, outcome definitions, and reporting, which limits the direct comparison of results. Before any integration into the clinical space, this technology requires testing under standardized protocols, validation in real-world and high-impact settings such as disasters and emergency medicine, and the definition of a clear distinction between statistically significant and clinically meaningful changes in payload parameters. Future primary work should dive into the granularity of identifying and characterizing which payloads, operational conditions, and environmental stressors pose genuine risks to the clinical utility of specific medical payloads.

This entry displays the Val.Es.Co. model for discourse segmentation in units and positions. In its latest updated version, the model offers a relation of eight discourse units and seven positions for analyzing spoken discourse. The last extension of the model for the segmentation of written texts is also introduced.

Indigenous knowledge stewardship in AI-enabled Technology Education refers to the community-authorised governance of Indigenous technological knowledge as it is created, validated, transmitted, applied, protected and renewed in educational settings. Indigenous technological knowledge includes situated understandings, skills, practices, artefacts and responsibilities developed through sustained relationships among communities, materials, technologies and environments. Stewardship differs from unrestricted collection or dissemination because authority remains with legitimate knowledge holders and community structures. In digital repositories, learning platforms and artificial intelligence systems, responsible stewardship calls for continuing, purpose-specific consent; accurate provenance; preservation of language and context; differentiated access; purpose limitation; transparency about indexing and model training; reciprocal benefit; and workable mechanisms for correction, withdrawal and refusal. Whether any of these safeguards is a legally enforceable right depends on the jurisdiction, instrument and agreement concerned; the definition therefore combines legal requirements, soft-law norms, community governance principles and recommended ethical practice. Human and community verification remains necessary where AI systems translate, classify or generate explanations of Indigenous knowledge. In Technology Education, these principles support culturally responsive investigation, design, making, evaluation and communication while advancing epistemic justice, intergenerational continuity and education for sustainable development.