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| http://dx.doi.org/10.1097/CIN.0000000000000210 | DOI Listing |
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Prospective evaluation of artificial intelligence (AI) applications for use in cancer pathways following diagnosis: a systematic review.
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Institute of Cancer Policy, King's College London Faculty of Life Sciences & Medicine, London, UK.
The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancement. Despite recent uptake in AI research and adoption, the extent to which it improves quality, efficiency and equity of care beyond cancer diagnostics is uncertain to date. Henceforth, the objective of our systematic review is to assess the clinical readiness and deployability of AI through evaluation of prospective studies of AI in cancer care following diagnosis.
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Database (Oxford)
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Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON CA K1A 0C6, Canada.
It is well-known that the use of vocabulary in phenotype treatments is often inconsistent. An earlier survey of biologists who create or use phenotypic characters revealed that this lack of standardization leads to ambiguities, frustrating both the consumers and producers of phenotypic data. Such ambiguities are challenging for biologists, and more so for Artificial Intelligence, to resolve.
View Article and Find Full Text PDFHealth networking on cancer in the European Union: a 'green paper' by the EU Joint Action on Networks of Expertise (JANE).
ESMO Open
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Evaluative Epidemiology Unit, Department of Epidemiology and Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Health networking is in principle a formidable instrument to address many challenges posed by cancer, one of the two most common and most lethal non-communicable chronic diseases. The European Union (EU)'s Beating Cancer Plan foresaw the addition of new health networks to the four already existing European Reference Networks on rare cancers: the Network of Comprehensive Cancer Centres and several networks of expertise (NoEs), which will be shortly deployed on items as complex and poor-prognosis cancers, palliative care, survivorship, personalised primary and secondary prevention, omic technologies, hi-tech medical resources, and cancers in adolescents and young adults. The community of experts of the EU Joint Action, due to build such NoEs, has drafted this 'green paper', incorporating 13 open questions, in an effort to foster discussion on some open questions about health networking on cancer in the EU.
View Article and Find Full Text PDFAddressing the overuse of hospital emergency departments in the Portuguese NHS: a new paradigm.
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Portuguese National Health Service Executive Board, Porto, Portugal.
The escalating trend of inappropriate visits to Emergency Departments (ED) has led to significant concerns, including resource misallocation, compromised patient care, and an increased burden on healthcare workers. Portugal faces a notable challenge, reporting one of the highest ED visit rates, with an annual average of approximately 6 million ED visits from 2013 to 2023. In response, the "Call First, Save Lives" pilot project was launched by the Portuguese NHS Executive Board, in 2023, at the Local Health Unit (LHU) of Póvoa de Varzim/Vila do Conde.
View Article and Find Full Text PDFProximal remote sensing: an essential tool for bridging the gap between high-resolution ecosystem monitoring and global ecology.
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Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91011, USA.
A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or 'proximal' remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site-level eddy-covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high-spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions.
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