ArticleEuropean radiology experimental2026
Bridging the AI chasm in oncology: a standardized platform to enable the in silico clinical validation of AI models within the CHAIMELEON Project.
Article in European radiology experimental, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06950996 (An in Silico External Clinical Validation of AI Solutions for Cancer Management in the CHAIMELEON Project. Applied to 4 Target Types of Cancer), which is not on this map. Not yet cited in PubMed.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
An in Silico External Clinical Validation of AI Solutions for Cancer Management in the CHAIMELEON Project. Applied to 4 Target Types of Cancer (Lung, Breast, Prostate and Colorectal), Collected Through the Routine Delivery of Health Care With no Enrolment Conditinos (Real World Data).
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10 authors.
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Abstract
objectiveArtificial intelligence (AI) shows promise for improving cancer management, but clinical adoption is limited by the absence of standardized validation tools. As an important step toward bridging the AI chasm, the European CHAIMELEON project addresses this gap by developing a platform for in silico validation of AI models in oncology, aligned with the EUCAIM project. MATERIALS AND
methodsWe designed a web-based platform using Kubernetes microservices architecture, integrating imaging, clinical, and AI components. The backend combines a REST API, an ORTHANC-PACS, and a Keycloak/OAuth2 security layer. The frontend includes a customized OHIF DICOM viewer for oncological case review. Clinicians evaluated cases across three sequential stages: (1) Standard clinical assessment, (2) Assessment with AI predictions, and (3) Final review against clinical endpoint ground truth. The platform also collects clinicians' perceptions of AI utility, trust, workflow integration, and usability through a Likert-scale questionnaire.
resultsThe platform provided efficient access to multimodal data for five cancer types. Clinicians completed structured validations, and survey responses indicated favorable perceptions: 93% found the platform intuitive, over 80% would recommend it, and agreement on AI utility exceeded 40% across most endpoints, indicating a positive reception of the AI as a supportive "second reader" tool that prompts clinical re-evaluation rather than simple consensus. Workflow impact was rated positively, underlining the potential to support clinical decision-making.
conclusionThis platform offers a reproducible, scalable, and user-friendly environment for clinical validation of AI models in oncology. Its modular architecture allows integration of additional AI models and is designed to ensure sustainability and interoperability, fostering evidence-based AI adoption in European radiology practice.
trial registrationClinicalTrials.gov, NCT06950996. KEY POINTS: How can the medical community standardize the in silico clinical validation of oncology AI models to safely bridge the gap before real-world deployment? We successfully implemented a web-based microservices platform, demonstrating high clinical usability and defining human-AI trust dynamics across five distinct cancer types. This standardized validation framework directly enhances diagnostic confidence and interdisciplinary medical collaboration, bridging technical performance with clinical trust to ensure that innovative artificial intelligence technologies safely optimize daily treatment decisions and ultimately maximize therapeutic success and safety for oncology patients.
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