PubMed · 11629197
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G Masuy-Stroobant. 1983. [Not Available].. https://pubmed.ncbi.nlm.nih.gov/11629197/
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PURPOSE: Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH: Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS: The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.
Allele frequencies and haplotypes for 12 Y-chromosomal STR loci included in the Powerplex System (Promega, Madison, USA) were determined in a sample of 113 unrelated males of Belgian origin. Ninety-nine different haplotypes were observed with an overall haplotype diversity of 0.997.
Environmental assessment models are used as decision-aiding tools in the selection of remediation options for radioactively contaminated sites. In most cases, the effectiveness of the remedial actions in terms of dose savings cannot be demonstrated directly, but can be established with the help of environmental assessment models, through the assessment of future radiological impacts. It should be emphasized that, given the complexity of the processes involved and our current understanding of how they operate, these models are simplified descriptions of the behaviour of radionuclides in the environment and therefore imperfect. One way of testing and improving the reliability of the models is to compare their predictions with real data and/or the predictions of other models. Within the framework of the Remediation Assessment Working Group (RAWG) of the BIOMASS (BIOsphere Modelling and ASSessment) programme coordinated by IAEA, two scenarios were constructed and applied to test the reliability of environmental assessment models when remedial actions are involved. As a test site, an area of approximately 100 ha contaminated by the discharges of an old radium extraction plant in Olen (Belgium) has been considered. In the first scenario, a real situation was evaluated and model predictions were compared with measured data. In the second scenario the model predictions for specific hypothetical but realistic situations were compared. Most of the biosphere models were not developed to assess the performance of remedial actions and had to be modified for this purpose. It was demonstrated clearly that the modeller's experience and familiarity with the mathematical model, the site and with the scenario play a very important role in the outcome of the model calculations. More model testing studies, preferably for real situations, are needed in order to improve the models and modelling methods and to expand the areas in which the models are applicable.