Abstract This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance. By proposing actionable solutions, the statement highlights ESR’s readiness in supporting appropriate application of the AI Act in the field, promoting clarity and the effective integration of AI technologies to ensure their impactful and safe use for the benefit of Europe’s patients. Critical relevance statement With the impending arrival of the EU AI Act, it is critical for stakeholders to provide timely input on its key areas. This statement offers expert feedback on the aspects of the EU AI Act that will affect medical imaging. Key Points The AI Act will significantly impact the field of medical imaging, shaping how AI technologies are used and regulated. The ESR is committed to develop guidelines and best practices, collaborating on the implementation process. This statement offers expert feedback on the aspects of the framework that will affect medical imaging. Graphical Abstract
The increasing integration of artificial intelligence as medical devices (AIaMDs) within diagnostic imaging necessitates a robust understanding of associated regulatory frameworks among clinical practitioners. Despite the growing commercial availability and adoption of AIaMD, a significant awareness gap persists among radiologists regarding pertinent European Union regulations, including the Medical Device Regulation (MDR) and the novel EU AI Act, both of which lack explicit provisions tailored to AI components. This regulatory ambiguity underscores a critical need for clarified guidelines concerning "high-risk" AI classification and best practices for safe deployment within the radiological workflow. Legal responsibility for AIaMD Post-Market Surveillance (PMS) primarily rests with software providers, yet radiologists are expected to contribute to the ongoing monitoring of safety and performance. Recognizing the need to raise awareness and provide practical guidance, the European Society of Radiology (ESR) eHealth and Informatics Subcommittee, supported by the ESR AI Working Group, conducted a modified Delphi procedure involving 16 domain experts (of which 14 acted as panelists) to establish a set of shared recommendations. These aim to establish essential practices for AIaMD PMS and post-market clinical feedback (PMCF), as stipulated by the MDR and partially updated by the AI Act. This paper also provides an overview of relevant regulations to enhance awareness among all stakeholders, particularly deployers (e.g., radiologists) and providers (e.g., vendors). These recommendations represent a foundational step towards improving consistency in AIaMD deployment, providing a critical reference standard for physicians navigating the unique challenges posed by these novel technologies. CRITICAL RELEVANCE STATEMENT: Radiologists need to familiarize themselves with AIaMD EU regulations due to shared PMS responsibilities and current ambiguities. ESR recommendations aim to bridge this awareness gap, standardizing safe AI deployment and enhancing clinical feedback within medical imaging. KEY POINTS: Radiologists need a clear understanding of EU regulations for AIaMDs, as current laws lack imaging-specific guidance. There is a shared responsibility for AIaMD safety, with radiologists contributing to PMS and clinical feedback systems. The ESR provides crucial recommendations to standardize AI deployment and improve clinical feedback in imaging.
Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline—such as image, application of image filters, and selection of feature extraction parameters—can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics.
The present study examined the association of self-perceived negative and positive ageism (PNA/PPA) by older adults in Greece on their quality of life (QoL) and the moderating effects of psychological capital (i.e., self-efficacy, optimism, hope, and resilience) and social support. This web-based cross-sectional study recruited 351 participants from the community through a convenience sampling method. Their mean age was 72.5 years (SD = 9.1), with the majority being women (62.7%), and married (45.3%). Participants completed online self-reported questionnaires on quality of life (The Μental Health Quality of Life; MHQoL), perceived ageism (Perceived Ageism Questionnaire; PAQ-8), social support (the Lubben Social Network Scale-6; LSNS-6), psychological resources (the Compound PsyCap Scale-12; CPC-12R), and socio-demographic characteristics. The results showed that PNA was more strongly correlated with QoL than PPA. QoL was predicted by PNA (negatively) and PPA (positively), as well as by social and psychological resources (positively). Psychological resources moderated the effects of both PNA and PPA on QoL, while social support moderated only the effects of PPA on QoL. Higher PNA was associated with lower QoL only for participants with low levels of psychological resources. In contrast, higher PPA was associated with higher QoL for those with low levels of psychological resources and high levels of social support. The study findings contribute to the literature on self-perceived negative and positive ageism and lay the groundwork for social policy guidelines aimed at developing interventions to enhance psychosocial resources, thereby improving the QoL for older adults.
A correction to this paper has been published: https://doi.org/10.1007/s00024-021-02692-4
