Panel Selection Research Articles

Odor emission measurements: The role of n-butanol as a reference material and origins of large inter-laboratory variability

Malodors negatively impact the quality of life of people who are exposed to them. In order to enforce legal emission limit values that aim to reduce these adverse effects, reliable methods for the quantification of odor emissions are needed. The standard EN 13725 describes a method to quantify odor concentration, which is based on the use of human assessors as sensors. Previously published work that studied the performance characteristics of this method indicates that the variability of results between different laboratories is higher than expected and that the uncertainty associated with this method may be underestimated. We analyze here a unique data set, which contains the response of 368 assessors that participated in 21 odor proficiency tests performed in 2016–2022. By investigating the response of the smallest unit of an odor measurement by EN 13725, namely the individual assessor, as opposed to the panel, we gain unprecedented insight into the sources of inter-laboratory variations in odor measurements. Our results indicate that the assessor sensitivity to n-butanol is not transferable to other odorants and odor mixtures, which is contrary to the assumption made in EN 13725. By Monte-Carlo simulation coupled with our data set, we investigate how the responses of individual assessors transfer to panel responses, focusing on the influence of panel size, panel selection strategy and number of rounds. We find that for the laboratories investigated, larger panels only modestly reduce the inter-laboratory variability and we attribute this effect to non-random selection of assessors for the panels of these laboratories. The present work provides new insight into the sources of inter-laboratory variability in odor emission monitoring and is expected to be useful in the further development of reliable methods for odor concentration measurements and odor abatement technology.

The Context Of Text: Harmonizing Multilingual Texts Of The WTO

Legal traditions of international institutions reflect how language shapes the interaction of individuals from various legal disciplines. Institutions like the European Union (EU), United Nations (UN) and World Trade Organization (WTO) cater to multilingualism by publishing different linguistic versions of their texts. These organizations have their own divisions to promote multilingualism – it is the General Assembly and Conference Management for the UN; the Directorate-General for Translation (DGT) in the EU; and the Language and Documentation Services Division (LDSD) for the WTO. In the context of the WTO, coordination among different language groups in drafting WTO documents appears to be lacking. In addition to creating hurdles in substantive law, issues of multilingualism at the WTO also affect the procedural aspects of the dispute. This may have implications on third-party rights, selection of panellists, and timely resolution of disputes, thereby influencing a party’s strategy in a dispute. Against this background, the authors explore the issue of multilingualism at the WTO vis-à-vis the UN and EU by adopting a comparative research methodology. The Article provides recommendations in the form of best practices for improvements at the WTO in the context of multilingualism. WTO, multilingualism, VCLT, translation, DSU, European Union, United Nations, Spanish, French, language

Quality–Cost–Environment Assessment of Sustainable Manufacturing of Photovoltaic Panels

This paper aims to develop an integrated Quality–Cost–Environmental (QCE) indicator for the selection of photovoltaic panels (PVps) offered to customers, considering the following criteria: (i) quality satisfaction; (ii) cost-effectiveness; and (iii) environmental impact throughout the life cycle. The proposed QCE indicator was developed within a framework that incorporated an FAHP (Fuzzy Analytic Hierarchy Process), cost-effectiveness analysis (CEA), and life cycle assessment (LCA). The model test confirmed its effectiveness in choosing a PVp which combines environmental friendliness throughout its entire life cycle with satisfactory quality and a reasonable purchase price for customers. The proposed model can be utilized by individuals, businesses, and public entities for the selection of high-quality, cost-efficient, and environmentally friendly PVps—thereby promoting sustainable development.

Abstract 3803: Spatial architecture of tumor and immune cell lineages in syngeneic mouse tumor tissues

Abstract Immune checkpoint blockade (ICB) therapies have demonstrated great clinical benefit in a variety of malignancies, but there are many patients that do not respond. Despite many preclinical and clinical studies, the mechanism of ICB is not completely understood. Development of novel therapies targeting other immunosuppressive mechanisms or combining ICB with conventional chemo or radio therapies have the potential to increase the number of patients that achieve durable responses. However, the optimal therapy or combination of therapies for a patient is likely influenced by which immunosuppressive mechanisms exist within the tumor microenvironment (TME) and the phenotype and function of tumor-infiltrating immune cells. To delineate the mechanisms of immunosuppression we selected FFPE tissue sections from three syngeneic murine cancer models and performed tumor-immune profiling to spatially understand the contributions of both immune cells and cancer cells. Mouse reactive antibodies targeting specific cellular populations will help identify the location of immune cells within the tumor site and can be used for proof-of-concept studies. This approach can lead to novel insights to synergize anti-tumor activity of ICB therapies and will help to identify differences between responders and non-responders. The Cell DIVE Multiplex Imaging Solution allows probing and imaging of dozens of biomarkers on a whole single tissue section with an iterative staining and dye inactivation workflow. At its core, Cell DIVE is a precise and adaptable open multiplexing solution that enables flexibility in antibody selection of biomarker panels used in a multiplexed imaging study. Cell Signaling Technology (CST®) has a broad portfolio of IHC-validated antibodies to detect key proteins in the TME, enabling immune cell detection and phenotyping in tissue. CST offers off the shelf (OTS), ready-to-ship antibody conjugates that have been verified to work on Cell DIVE and offers custom conjugation of IHC-validated antibodies to fluorophores and other detection reagents. CST employs a rigorous approach to IHC validation, followed by verification on the Cell DIVE platform to ensure successful detection of proteins. Here, we demonstrate multiplexed Cell DIVE imaging using a novel panel of dozens of CST biomarker antibodies across multiple mouse syngeneic tumor types. In summary, multiplexed whole slide imaging using validated antibodies and Aivia AI-based image processing software enables spatially resolved tissue analysis of the tumor microenvironment, including new insights into spatial biology. Citation Format: ARINDAM BOSE, Lisa Arvidson, Emily Quann Alonzo, Michael J. Smith, Katie O. White, Richard A. Heil-Chapdelaine, Gabriella Spang, Matthew Norton, Samuel Jensen, Vasundhara Agrawal. Spatial architecture of tumor and immune cell lineages in syngeneic mouse tumor tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3803.

Research on material selection and low-temperature anti-cracking mechanism of hydraulic asphalt concrete panels in the alpine region

To solve the problem of asphalt concrete panel cracking at low temperatures, this study proposes to use crumb rubber as a modified material for asphalt in the impermeable layer of asphalt concrete panels and to investigate the anti-cracking performance of crumb rubber modified asphalt concrete (CRMAC) in water conservancy projects in different low-temperature environments. Taking matrix asphalt concrete (MAC), CRMAC, and SBS modified asphalt concrete (SBSMAC) as the research objects, the cracking resistance performance of the three kinds of asphalt concrete under different low-temperature conditions were comparatively analyzed by using ultra-low-temperature (−35°C) small beam bending test, which reveals the damage mechanism of asphalt concrete's low-temperature cracking and damage process; the failure temperatures of the three kinds of asphalt concrete were accurately quantified by the thermal stress restrained specimen test (TSRST), and combined with the Pearson correlation coefficient method to determine the evaluation criteria of asphalt concrete low-temperature performance. The results show that the low-temperature crack resistance of CRMAC is better than the other two asphalt concretes under the same temperature conditions. Its failure temperature reaches −43°C, which meets the low-temperature demand for asphalt concrete panels in alpine regions. This study aims to improve the low-temperature cracking resistance of asphalt concrete impermeable panels, provide new ideas for the selection of asphalt concrete panels, and lay the foundation for the application of CRMA in water conservancy projects, and open up new ways to solve the problem of waste tire accumulation.

Analyzing the degree persistence of shocks to energy security of the G7 countries: Evidence using panel SPSM-quantile unit root test

Energy security is affected by extreme natural, human, domestic political, geopolitical, and fossil energy price shocks/events and green energy policies. The degree of persistence in energy security determines the magnitudes of social, economic, and environmental outcomes of the shocks/policies. In this paper, we examined the degree of persistence in energy security of G7 countries using a new proxy namely the energy security risk index, and a novel second-generation panel quantile unit root test over the period 1980–2018. In addition, we applied the sequential panel selection method (SPSM), to identify the stationary members within each quantile. Our results indicated the stochastic properties of the energy security risk indexes vary across the quantile and the countries. Among the G7 countries, the energy security risk index of the US displays unit root process within all quantiles. While the energy security risk indexes of other countries display stationary processes, especially within high quantiles. Our results have important policy implications regarding the effectiveness of green policies in improving the energy security of the G7 countries and the disturbance effects of anti-energy security shocks. According to our findings, the US has to constantly pursue the risks that threaten the country's energy system while other G7 countries likely do not have such severe concerns about shocks affecting energy security, and these shocks have a short-term effect on their energy security.

Aggregation operators on group-based generalized q-rung orthopair fuzzy N-soft sets and applications in solar panel evaluation

Every problem in decision-making has a solution when the information that is available is properly and precisely modeled. This study focuses on non-binary data from N-soft sets and q-rung orthopair fuzzy values, referred to as group-based generalized q-rung orthopair fuzzy N-soft sets (GGq-ROFNSSs). The GGq-ROFNSSs model provides information simultaneously on numerous competing criteria, alternatives, sub-alternatives, and data summarization. We introduce properties of GGq-ROFNSSs such as distinct inclusion features of GGq-ROFNSSs, weak complements of the GGq-ROFNSS, top weak complements the GGq-ROFNSS, bottom weak complements the GGq-ROFNSS. We provide the notion of GGq-ROFNSWA and GGq-ROFNSWG operators as well as their idempotency, monotonicity, and boundedness features. The notion of GGq-ROFNSSs requires a sound methodology of multiple criteria decision making (MCDM) since GGq-ROFNSS combines numerous elements of complex decision-making. We provide a MCDM methodology for the GGq-ROFNSWA and GGq-ROFNSWG operators and depict it in a flowchart. The selection of solar panels for a city is a difficult procedure because it depends on several components such as environment, where the area is located, what kinds of needs are being met, etc. We find a solution to the problem of selecting a suitable solar panel for a city with their underlying characteristics. Finally, we provide a comparison of the suggested method with other techniques to demonstrate its advantages.

Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease

Chronic kidney disease (CKD) is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications. The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression. Herein, we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies, i.e., widely-targeted metabolomics (WT-Met) approach. WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites. We collected plasma samples from 21 healthy controls and 62 CKD patients, categorized into different stages (22 in stages 1–3, 20 in stage 4, and 20 in stage 5). Using LC-MS-based WT-Met approach, we were able to effectively annotate and quantify a total of 1431 metabolites from the plasma samples. Focusing on the 539 endogenous metabolites, we identified 399 significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD. Furthermore, we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD. We generated a multiclass classifier consisting of 7 metabolites by machine-learning, which exhibited an average AUC of 0.99 for the test set. In general, amino acids, nucleotides, organic acids, and their metabolites emerged as the most significantly altered metabolites. However, their patterns of change varied across different stages of CKD. The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD. Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD.

Health estimate differences between six independent web surveys: different web surveys, different results?

Most general population web surveys are based on online panels maintained by commercial survey agencies. Many of these panels are based on non-probability samples. However, survey agencies differ in their panel selection and management strategies. Little is known if these different strategies cause differences in survey estimates. This paper presents the results of a systematic study designed to analyze the differences in web survey results between agencies. Six different survey agencies were commissioned with the same web survey using an identical standardized questionnaire covering factual health items. Five surveys were fielded at the same time. A calibration approach was used to control the effect of demographics on the outcome. Overall, the results show differences between probability and non-probability surveys in health estimates, which were reduced but not eliminated by weighting. Furthermore, the differences between non-probability surveys before and after weighting are larger than expected between random samples from the same population.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Abstract B064: Redefining phenotypic heterogeneity of pancreatic ductal adenocarcinoma: A bottom-up approach

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) tumor inter-patient heterogeneity has been well described with two major subtypes (classical and basal-like). An important intra-patient heterogeneity has not yet been extensively studied due to the lack of standardized and easily accessible high throughput methods compatible with clinical routine. Material and Methods: To build an immunohistochemical (IHC) tool capable of differentiating classical and basal-like tumors, relevant antibodies were selected using a multi-step process. The successive stages of an in-silico selection from the bulk transcriptome of 309 PDACs and 30 patient-derived xenografts followed by the validation on tissue microarrays in a cohort of PDAC (n=50) were conducted. We used our final IHC panel on whole slides of serial histology sections in 95 resected PDACs. After image registration, we performed a tile-based-analysis in tumoral and pre-neoplastic areas and a k-means clustering to identify relevant marker combinations. Results: Our final selection of the IHC panel included GATA6, CLN18, TFF1, MUC16, S100A2, KRT17, PanBasal. Among the 44,042 tumor tiles analyzed, 4 different phenotypes were identified: 1 classical (CLN18+, TFF1+, GATA6+), 1 intermediate (KRT17+) and 2 basal-like (MUC16+ vs S100A2+) with specific biological properties. A minor basal contingent drastically reduced overall survival (OS) in the multivariate analysis (HR=2.36, p=0.01). In contrast, the presence of the intermediate and classical clusters did not impact OS. However, among patients with PDAC expressing the classical cluster, a tremendous difference of OS was observed according to the co-occurence of a basal cluster (HR=0.34, p=0.003). These prognostic associations have been validated in an independent cohort of 150 patients with PDAC on tissue microarrays. By analyzing preneoplastic lesions, we showed the evolution process of pancreatic carcinogenesis going from a classical toward a basal through an early intermediate phenotype. Conclusion: Our restricted panel of antibodies could help pathologists to predict the high degree of PDAC heterogeneity, ultimately leading to an improvement of patient management. Citation Format: Marc Hilmi, Flore Delecourt, Jerôme Rafenne, Taib Bourega, Nelson Dusetti, Juan Iovanna, Yuna Blum, Magali Richard, Cindy Neuzillet, Anne Couvelard, Louis De Mestier, Vinciane Rebours, Rémy Nicolle, Jérôme Cros. Redefining phenotypic heterogeneity of pancreatic ductal adenocarcinoma: A bottom-up approach [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B064.

The use of model samples in the process of selection of sensory panel to assess cosmetic products

AbstractThe article discusses an innovative approach to the formation of a sensory panel formed for the purpose of evaluating cosmetic products. Model samples of cosmetic emulsions are proposed, which should help in the initial selection and training of sensory panelists, in order to improve the precision of evaluations. Simple and easily reproducible emulsions (comprising rapeseed oil, sunflower lecithin, water, and xanthan gum) were formulated to represent a specific level of variability associated with a particular property, such as thickness. To confirm that the ability to rank the produced emulsions can be considered a criterion for selecting individuals for the sensory panel—resulting in greater consistency of results—tests involving moisturizing creams were conducted. The study's findings demonstrate that the novel approach to candidate selection, through the assessment of model emulsion consistency, is both effective and justified. This method has the potential to enhance recruitment efficiency by identifying individuals with a high ability to evaluate cosmetic emulsions.Practical ApplicationsThose who benefit from the work include professionals and researchers dedicated to enhancing sensory panel selection processes. The main goal was to optimize the sensory panel selection and training process. Reference samples help determine whether potential members have the right skills and potential for sensory evaluation. Selecting individuals with a greater aptitude for assessments based on the sense of touch can be particularly valuable for companies operating in industries where the sensory quality of products is important.

Decision-making for solar panel selection using Sugeno-Weber triangular norm-based on q-rung orthopair fuzzy information

Solar power is an alternative energy derived from the sun. Solar power is more environmentally friendly and sustainable than burning fossil fuels which releases harmful greenhouse gas emissions. Therefore, this study aims to evaluate a reliable solar panel based on certain characteristics by incorporating the theory of the decision-making process. To serve this goal, this study discusses a well-known aggregation model of the q-rung orthopair fuzzy set, which is a broader and flexible environment of fuzzy sets and intuitionistic fuzzy sets used to handle unpredictable information of human opinions. The key components of this article are to demonstrate some realistic operations of Sugeno–Weber triangular norms considering q-rung orthopair fuzzy information. These operations provide authentic estimated information during the decision-making process. We developed a class of new aggregation operators using the q-rung orthopair fuzzy information system, including q-rung orthopair fuzzy Sugeno–Weber power weighted average and q-rung orthopair fuzzy Sugeno–Weber power weighted geometric operators. Some realistic characteristics and special cases are also demonstrated to show the compatibility of the proposed methodologies. An innovative approach to the multi-attribute decision-making problem is utilized to resolve different real-life applications considering various criteria or attributes. To show the intensity and applicability of the proposed approaches, we explored a numerical example for efficient solar panel selection based on the proposed methodologies. Furthermore, we presented a comprehensive comparison technique to compare the findings of the existing methods with the proposed aggregation approaches. Finally, the proposed research work is summarized, and the future prospects are discussed.

Comparing Brain and Blood Lipidome Changes following Single and Repetitive Mild Traumatic Brain Injury in Rats.

Traumatic brain injury (TBI) is a major health concern in the United States and globally, contributing to disability and long-term neurological problems. Lipid dysregulation after TBI is underexplored, and a better understanding of lipid turnover and degradation could point to novel biomarker candidates and therapeutic targets. Here, we investigated overlapping lipidome changes in the brain and blood using a data-driven discovery approach to understand lipid alterations in the brain and serum compartments acutely following mild TBI (mTBI) and the potential efflux of brain lipids to peripheral blood. The cortices and sera from male and female Sprague-Dawley rats were analyzed via ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in both positive and negative ion modes following single and repetitive closed head impacts. The overlapping lipids in the data sets were identified with an in-house data dictionary for investigating lipid class changes. MS-based lipid profiling revealed overall increased changes in the serum compartment, while the brain lipids primarily showed decreased changes. Interestingly, there were prominent alterations in the sphingolipid class in the brain and blood compartments after single and repetitive injury, which may suggest efflux of brain sphingolipids into the blood after TBI. Genetic algorithms were used for predictive panel selection to classify injured and control samples with high sensitivity and specificity. These overlapping lipid panels primarily mapped to the glycerophospholipid metabolism pathway with Benjamini-Hochberg adjusted q-values less than 0.05. Collectively, these results detail overlapping lipidome changes following mTBI in the brain and blood compartments, increasing our understanding of TBI-related lipid dysregulation while identifying novel biomarker candidates.

Confidence Levels-Based p, q, r–Spherical Fuzzy Aggregation Operators and Their Application in Selection of Solar Panels

Confidence Levels-Based <i>p</i>, <i>q</i>, <i>r</i>–Spherical Fuzzy Aggregation Operators and Their Application in Selection of Solar Panels

Testing for stationary of housing prices in China: An examination using efficient unit root tests

Changes in housing prices affect all aspects of production and life, and have always been a hot spot of social concern. This paper uses the sequence panel selection method (SPSM) to study the time series properties of housing prices in 100 cities in China from June 2010 to December 2022. It is found that there are large differences in the stationary of housing prices in first/second/third-tier cities. Using the SPSM test method, it is found that housing prices in first-tier cities are all non-stationary series, the samples of second- and third-tier cities can be significantly divided into stable housing prices and non-stable housing prices. After further using the Fourier function to approximate the structural mutation of the data, more second-tier cities show stable housing prices, while less third-tier cities show stable housing prices. These findings provide an important decision-making basis for the government to implement regulatory policies according to local conditions based on the differential characteristics of changes in housing prices.

Quantitative flow cytometry enables end-to-end optimization of cross-platform extracellular vesicle studies

Flow cytometry (FCM) is a common method for characterizing extracellular particles (EPs), including viruses and extracellular vesicles (EVs). Frameworks such as MIFlowCyt-EV exist to provide reporting guidelines for metadata, controls, and data reporting. However, tools to optimize FCM for EP analysis in a systematic and quantitative way are lacking. Here, we demonstrate a cohesive set of methods and software tools that optimize FCM settings and facilitate cross-platform comparisons for EP studies. We introduce an automated small-particle optimization (SPOT) pipeline to optimize FCM fluorescence and light scatter detector settings for EP analysis and leverage quantitative FCM (qFCM) as a tool to further enable FCM optimization of fluorophore panel selection, laser power, pulse statistics, and window extensions. Finally, we demonstrate the value of qFCM to facilitate standardized cross-platform comparisons, irrespective of instrument configuration, settings, and sensitivity, in a cross-platform standardization study utilizing a commercially available EV reference material.

Editors' perspectives on the selection of reviewers and the quality of reviews.

This article describes the outcomes of a survey of 93 editors in chief and associate editors of behavior-analytic journals. We sought information about variables that influence their judgment of the selection of reviewers, selection of review panels, and quality of reviews. When selecting reviewers, participants rated highly expertise on the topic, history of conducting good reviews, and history of writing constructive and respectful reviews. When selecting review panels, participants rated highly stratifying reviewers based on their expertise, avoiding conflicts of interest, and the matching based on the area of expertise between reviewers and authors. When evaluating the quality of a review, participants rated highly considerations related to research design, the science underlying the main idea, and accurate interpretations of the data. Participants did not rate copyediting as important. Overall, the extent to which reviewer selection was influenced by membership in underrepresented groups varied. These findings can inform the development of training programs for teaching peer-review repertoires.

An integrated picture fuzzy Z-AHP & TOPSIS methodology: Application to solar panel selection

AHP is the most popular multi-criteria decision-making (MCDM) method to prioritize the evaluation criteria and to rank the considered alternatives. TOPSIS method is another often used MCDM method based on negative and positive ideal solutions for the selection of the best alternative. Although the picture fuzzy extensions of the TOPSIS method are used to consider experts’ hesitancy and refusal degrees in their judgments, there is a need to add reliability degrees to these judgments to provide better solutions and a reliable decision environment for real-life decision problems. This study presents a decision support tool that integrates the picture fuzzy Z-AHP (PF Z-AHP) method based on pairwise comparisons and a novel PF Z-TOPSIS method which includes reliability information for ranking the alternatives. The proposed PF Z-AHP&TOPSIS methodology is applied for solar energy panel selection problem to analyze the practicality and superiority of the methodology. The sensitivity analysis demonstrates the robustness of the proposed methodology, and the comparison analysis presents evidence that the reliability functions offered by Z-numbers have the potential to affect outcomes.

French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)

PurposeThe purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology. Materials and methodsThe RAND-UCLA Appropriateness Method was chosen by expert radiologists from the DRIM France IA group for this statement paper. The study, initiated by the radiology community, involved seven steps including literature review, template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting. ResultsThe panel consisted of seven software vendors, three for bone fracture detection using conventional radiology and four for breast cancer detection using mammography. A consensus was reached on various aspects, including general target, main objective, certification marking, integration, expression of results, forensic aspects and cybersecurity, performance and scientific validation, description of the company and economic details, possible usage scenarios in the clinical workflow, database, specific objectives and targets of the AI tool. ConclusionThe study validates a descriptive and analytical grid for radiological AI solutions consisting of ten items, using breast cancer and bone fracture as an experimental guide. This grid would assist radiologists in selecting relevant and validated AI solutions. Further developments of the grid are needed to include other organs and tasks.