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INTRODUCTION: Artificial intelligence (AI) has started to be increasingly adopted in medical imaging and radiotherapy clinical practice, however research, education and partnerships have not really caught up yet to facilitate a safe and effective transition. The aim of the document is to provide baseline guidance for radiographers working in the field of AI in education, research, clinical practice and stakeholder partnerships. The guideline is intended for use by the multi-professional clinical imaging and radiotherapy teams, including all staff, volunteers, students and learners. METHODS: The format mirrored similar publications from other SCoR working groups in the past. The recommendations have been subject to a rapid period of peer, professional and patient assessment and review. Feedback was sought from a range of SoR members and advisory groups, as well as from the SoR director of professional policy, as well as from external experts. Amendments were then made in line with feedback received and a final consensus was reached. RESULTS: AI is an innovative tool radiographers will need to engage with to ensure a safe and efficient clinical service in imaging and radiotherapy. Educational provisions will need to be proportionately adjusted by Higher Education Institutions (HEIs) to offer the necessary knowledge, skills and competences for diagnostic and therapeutic radiographers, to enable them to navigate a future where AI will be central to patient diagnosis and treatment pathways. Radiography-led research in AI should address key clinical challenges and enable radiographers co-design, implement and validate AI solutions. Partnerships are key in ensuring the contribution of radiographers is integrated into healthcare AI ecosystems for the benefit of the patients and service users. CONCLUSION: Radiography is starting to work towards a future with AI-enabled healthcare. This guidance offers some recommendations for different areas of radiography practice. There is a need to update our educational curricula, rethink our research priorities, forge new strong clinical-academic-industry partnerships to optimise clinical practice. Specific recommendations in relation to clinical practice, education, research and the forging of partnerships with key stakeholders are discussed, with potential impact on policy and practice in all these domains. These recommendations aim to serve as baseline guidance for UK radiographers. IMPLICATIONS FOR PRACTICE: This review offers the most up-to-date recommendations for clinical practitioners, researchers, academics and service users of clinical imaging and therapeutic radiography services. Radiography practice, education and research must gradually adjust to AI-enabled healthcare systems to ensure gains of AI technologies are maximised and challenges and risks are minimised. This guidance will need to be updated regularly given the fast-changing pace of AI development and innovation.
Assuntos
Inteligência Artificial , Radiologia , Pessoal Técnico de Saúde , Ecossistema , Humanos , RadiografiaRESUMO
A computer-based data network containing information on potentially toxic chemicals released into the environment is being developed by the Joint Research Centre of the Commission of the European Communities and by research institutions in the member states as part of a program for environmental research. The information requested daily by toxicologists involved in administration, scientific research, and clinical or forensic toxicology covers different fields or disciplines. The data base, which is still in a pilot phase, collects data on environmental chemicals (about 30,000) spread over more than 100 data fields. These include identification; physical and chemical properties; analytical methods; economic data; hazard classifications for transport, handling and storage; waste disposal; environmental dispersion and transformation; toxicology: acute and chronic effects, carcinogenicity, mutagenicity, behavioral effects; occupational air standards, first-aid treatments in case of poisoning or environmental disaster. Data stored in the data bank are original literature data which have been evaluated by specialists.
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Serviços de Informação sobre Medicamentos , Toxicologia , Coleta de Dados , Arquivamento , HumanosRESUMO
C-Phycocyanin was isolated and purified from a uni-algal culture of an extremely halo-tolerant blue-green alga, Coccochloris elabens. This alga can be grown under laboratory conditions in 25% (w/v) NaCl. Purified halophile phycocyanin was characterized by amino acid analysis and the measurement of sedimentation velocity, fluorescence polarization and immunodiffusion as a function of protein concentration, pH and ionic strength. The results were compared with those of studies of phycocyanin isolated from Plectonema calothricoides and from several other sources. The states of aggregation previously characterized as being present in other C-phycocyanins, monomer, trimer and hexamer, were present in halophile phycocyanin and were characterized as antigenically related to all C-phycocyanins tested. The equilibrium between 3S monomer and 11S hexamer at low concentrations in halophile phycocyanin was quantitatively similar to that for other phycocyanins. The effect of pH and ionic strength on the 6S (trimer) and 11S (hexamer) aggregation of halophile phycocyanin was markedly salt-dependent and the relative amount of each aggregate in the presence of 2m-NaCl was like that of C-phycocyanin from mesophiles, in the absence of additional salt. In antigenic relationship and aggregation properties, the phycocyanin from C. elabens appeared to be most closely related to that isolated from the thermophilic blue-green alga, Synechococcus lividus. Amino acid content of the halophile phycocyanin indicated the presence of a significantly larger number of acidic residues than that found in mesophiles. Explanations of the properties of the halophile protein require consideration of a strong contribution of hydrophobic forces and utilize both charge-shielding and salting-out effects.
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Cianobactérias/análise , Proteínas de Plantas/análise , Aminoácidos/análise , Animais , Contagem de Células , Centrifugação com Gradiente de Concentração , Temperatura Alta , Concentração de Íons de Hidrogênio , Imunodifusão , Focalização Isoelétrica , Substâncias Macromoleculares , Proteínas de Plantas/isolamento & purificação , Coelhos/imunologia , Cloreto de Sódio , Espectrometria de Fluorescência , Espectrofotometria , Espectrofotometria UltravioletaRESUMO
A data bank for environmental chemicals, ECDIN, is being developed at the Joint Research Centre of the European Communities in cooperation with universities and research institutes in the nine member states as a part of the Environmental Research Programme of the EC. During the pilot phase of the project, data from the Registry of Toxic Effects of Chemical Substances have been incorporated into the data bank. Conversions of the data into ECDIN input format was necessary before inclusion of the toxicity data in ECDIN, and the computer programs used for this format conversion have produced various statistics for the contents of the RTECS files. Analyses of the data in three editions of RTECS are presented.