Dataset Information

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

ABSTRACT: The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.

SUBMITTER: Bonmati LM

PROVIDER: S-EPMC8913333 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

Bonmatí Luis Martí LM Miguel Ana A Suárez Amelia A Aznar Mario M Beregi Jean Paul JP Fournier Laure L Neri Emanuele E Laghi Andrea A França Manuela M Sardanelli Francesco F Penzkofer Tobias T Lambin Phillipe P Blanquer Ignacio I Menzel Marion I MI Seymour Karine K Figueiras Sergio S Krischak Katharina K Martínez Ricard R Mirsky Yisroel Y Yang Guang G Alberich-Bayarri Ángel Á

Frontiers in oncology 20220224

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, brea ...[more]

PMID: 35280732

Similar Datasets

Project description:BackgroundArtificial intelligence (AI) has the potential to revolutionize health care by enhancing both clinical outcomes and operational efficiency. However, its clinical adoption has been slower than anticipated, largely due to the absence of comprehensive evaluation frameworks. Existing frameworks remain insufficient and tend to emphasize technical metrics such as accuracy and validation, while overlooking critical real-world factors such as clinical impact, integration, and economic sustainability. This narrow focus prevents AI tools from being effectively implemented, limiting their broader impact and long-term viability in clinical practice.ObjectiveThis study aimed to create a framework for assessing AI in health care, extending beyond technical metrics to incorporate social and organizational dimensions. The framework was developed by systematically reviewing, analyzing, and synthesizing the evaluation criteria necessary for successful implementation, focusing on the long-term real-world impact of AI in clinical practice.MethodsA search was performed in July 2024 across the PubMed, Cochrane, Scopus, and IEEE Xplore databases to identify relevant studies published in English between January 2019 and mid-July 2024, yielding 3528 results, among which 44 studies met the inclusion criteria. The systematic review followed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines and the Cochrane Handbook for Systematic Reviews. Data were analyzed using NVivo through thematic analysis and narrative synthesis to identify key emergent themes in the studies.ResultsBy synthesizing the included studies, we developed a framework that goes beyond the traditional focus on technical metrics or study-level methodologies. It integrates clinical context and real-world implementation factors, offering a more comprehensive approach to evaluating AI tools. With our focus on assessing the long-term real-world impact of AI technologies in health care, we named the framework AI for IMPACTS. The criteria are organized into seven key clusters, each corresponding to a letter in the acronym: (1) I-integration, interoperability, and workflow; (2) M-monitoring, governance, and accountability; (3) P-performance and quality metrics; (4) A-acceptability, trust, and training; (5) C-cost and economic evaluation; (6) T-technological safety and transparency; and (7) S-scalability and impact. These are further broken down into 28 specific subcriteria.ConclusionsThe AI for IMPACTS framework offers a holistic approach to evaluate the long-term real-world impact of AI tools in the heterogeneous and challenging health care context and lays the groundwork for further validation through expert consensus and testing of the framework in real-world health care settings. It is important to emphasize that multidisciplinary expertise is essential for assessment, yet many assessors lack the necessary training. In addition, traditional evaluation methods struggle to keep pace with AI's rapid development. To ensure successful AI integration, flexible, fast-tracked assessment processes and proper assessor training are needed to maintain rigorous standards while adapting to AI's dynamic evolution.Trial registrationreviewregistry1859; https://tinyurl.com/ysn2d7sh.

Project description:BackgroundArtificial Intelligence is currently being applied in healthcare for diagnosis, decision-making and education. ChatGPT-4o, with its advanced language and problem-solving capabilities, offers an innovative alternative as a virtual standardised patient in clinical training. Intern physicians are expected to develop clinical case management skills such as problem-solving, clinical reasoning and crisis management. In this study, ChatGPT-4o's served as virtual standardised patient and medical interns as physicians on clinical case management. This study aimed to evaluate intern physicians' competencies in clinical case management; problem-solving, clinical reasoning, crisis management and explore the impact and potential of ChatGPT-4o as a viable tool for assessing these competencies.MethodsThis study used a simultaneous triangulation design, integrating quantitative and qualitative data. Conducted at Aydın Adnan Menderes University, with 21 sixth-year medical students, ChatGPT-4o simulated realistic patient interactions requiring competencies in clinical case management; problem-solving, clinical reasoning, crisis management. Data were gathered through self-assessment survey, semi-structured interviews, observations of the students and ChatGPT-4o during the process. Analyses included Pearson correlation, Chi-square, and Kruskal-Wallis tests, with content analysis conducted on qualitative data using MAXQDA software for coding.ResultsAccording to the findings, observation and self-assessment survey scores of intern physicians' clinical case management skills were positively correlated. There was a significant gap between participants' self-assessment and actual performance, indicating discrepancies in self-perceived versus real clinical competence. Participants reported feeling inadequate in their problem-solving and clinical reasoning competencies and experienced time pressure. They were satisfied with the Artificial Intelligence-powered standardised patient process and were willing to continue similar practices. Participants engaged with a uniform patient experience. Although participants were satisfied, the application process was sometimes negatively affected due to disconnection problems and language processing challenges.ConclusionsChatGPT-4o successfully simulated patient interactions, providing a controlled environment without risking harm to real patients for practicing clinical case management. Although some of the technological challenges limited effectiveness, it was useful, cost-effective and accessible. It is thought that intern physicians will be better supported in acquiring clinical management skills through varied clinical scenarios using this method.Clinical trial numberNot applicable.

Project description:Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.

Project description:Background: Pharmacist-led medication reviews (MR) are one of the key methods to support medication safety in polypharmacy patients. The aims of this study were to pilot MRs in Eastern European community pharmacies, describe medication use in polypharmacy patients, and evaluate the usability of medication safety assessment tools. Methods: The MR pilot was undertaken in Estonia, Latvia, Poland, Hungary, Romania, and Bulgaria. Patients who used at least five medicines were directed to the service by their GPs. Data on drug-related problems (DRPs) and adherence were collected by pharmacists through structured patient interviews. Databases for identification of potential drug-drug interactions (pDDIs) and adverse drug reactions (ADRs) named Inxbase/Riskbase, as well as an integrated tool comprising potentially inappropriate medicines (PIMs) lists EU(7)-PIM and EURO-FORTA, were applied retroactively to the MR pilot data to investigate possibilities for their use and to describe medication use and potential risks in the study population. Results: A total of 318 patients were included in the study, 250 of them elderly (≥65 years). One hundred and eighty (56.6%) participants had a total of 504 pDDIs based on Inxbase analysis. On average, there were 1.6 pDDIs per participant. Twenty-five (5.0%) of the 504 pDDIs were in a high-risk category. A total of 279 (87.7%) participants had a potential ADR in at least one of 10 Riskbase categories. One hundred and fifty-four (20.8%) of the potential ADRs were in a high-risk category. Twenty-seven pDDIs and 68 ADRs documented as DRPs during the service were not included in the databases. Using the integrated EU(7)-PIM/EURO-FORTA PIM list, a total of 816 PIMs were found in 240 (96%) of the 250 elderly participants (on average 3.4 PIMs per elderly participant). Seventy-one (29.6%) of the participants were using high-risk PIMs. Twenty-one percent of high-risk PIMs and 13.8% of medium-risk PIMs were documented as DRPs by the pharmacists during the pilot. Conclusion: Medication safety assessment tools can be useful in guiding decision-making during MRs; however, these tools cannot replace patient interviews and monitoring. Tools that include a thorough explanation of the potential risks and are easy to use are more beneficial for MRs.

Dataset Information

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

Publications

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets