To review eXplainable Artificial Intelligence/(XAI) methods available for medical imaging/(MI). A scoping review was conducted following the Joanna Briggs Institute's methodology. The search was performed on Pubmed, Embase, Cinhal, Web of Science, BioRxiv, MedRxiv, and Google Scholar. Studies published in French and English after 2017 were included. Keyword combinations and descriptors related to explainability, and MI modalities were employed. Two independent reviewers screened abstracts, titles and full text, resolving differences through discussion. 228 studies met the criteria. XAI publications are increasing, targeting MRI (n = 73), radiography (n = 47), CT (n = 46). Lung (n = 82) and brain (n = 74) pathologies, Covid-19 (n = 48), Alzheimer's disease (n = 25), brain tumors (n = 15) are the main pathologies explained. Explanations are presented visually (n = 186), numerically (n = 67), rule-based (n = 11), textually (n = 11), and example-based (n = 6). Commonly explained tasks include classification (n = 89), prediction (n = 47), diagnosis (n = 39), detection (n = 29), segmentation (n = 13), and image quality improvement (n = 6). The most frequently provided explanations were local (78.1 %), 5.7 % were global, and 16.2 % combined both local and global approaches. Post-hoc approaches were predominantly employed. The used terminology varied, sometimes indistinctively using explainable (n = 207), interpretable (n = 187), understandable (n = 112), transparent (n = 61), reliable (n = 31), and intelligible (n = 3). The number of XAI publications in medical imaging is increasing, primarily focusing on applying XAI techniques to MRI, CT, and radiography for classifying and predicting lung and brain pathologies. Visual and numerical output formats are predominantly used. Terminology standardisation remains a challenge, as terms like "explainable" and "interpretable" are sometimes being used indistinctively. Future XAI development should consider user needs and perspectives.

Global warming stands as a paramount public health issue of our time, and it is fundamental to explore approaches to green medical imaging departments/(MID). This study aims to map the existing actions in the literature that promote sustainable development in MID towards the promotion of environmental impact reduction. Following the JBI methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), this literature search was conducted on MEDLINE, Embase and CINAHL to encompass studies published after 2013. Combinations of keywords and relevant terms related to environmental sustainability, recycling, medical waste, and greening radiology were applied for this review. Three independent reviewers screened abstracts, titles, and eligible full-text. Disagreement was solved through consensus. 38 out of 4630 articles met all inclusion criteria, and four additional articles were identified and added through reference search. A third of the studies included were published after 2022, and most were conducted in developed countries (36/41). Articles focused on computed tomography (9/41), magnetic resonance imaging (6/41), interventional radiology (4/41), conventional radiography (4/41), ultrasound (2/41), mixed modalities (10/41), or not applicable to an imaging modality (6/41). Four principal categories were identified to decrease ecological footprint: energy consumption, waste management, justification and environmental pollution. To minimise the environmental impact of MIDs raising awareness and promoting education is fundamental. Examinations must be justified adequately, energy consumption must be reduced, and waste management practices need to be implemented. Further studies are required to prioritise the most effective strategies, supporting decision-making among stakeholders. Several strategies are already possible to implement to reduce the environmental impact of MIDs and improve the healthcare outcomes for patients.

Sustainable development is an approach that must be applied to all areas within healthcare, especially those in constant technological development, such as nuclear medicine. The aim of this study was to scope the literature to identify knowledge gaps in the sustainable development of the nuclear medicine field. The Joanna Briggs Institute methodology for scoping reviews was employed. A search strategy was conducted in PubMed, Embase, CINAHL and Web of Science in December 2024 using Mesh terms and keywords related to sustainability development and nuclear medicine. Three independent reviewers screened titles, abstracts, and full text. Four authors performed the data extraction and analysis. A total of 36 studies met the inclusion criteria. The majority originated from Spain, and most were published in 2022 and 2023. Studies were categorised by procedure type: 33.3 % focused on therapy, 9.7 % on PET imaging, 33.3 % on diagnostic and therapeutic procedures, and 18.1 % on SPECT imaging. They were also categorised by location to include research conducted in hospital settings (n = 23), non-hospital settings (n = 8), both (n = 4) or none (n = 1). Reviewed studies implemented sustainability measures across the sustainability pillars: environmental (n = 30), economic (n = 12), and social (n = 9) pillars. This scoping review has highlighted the growing interest in sustainability, within the nuclear medicine field. Findings indicate that there is a strong emphasis on reducing environmental impact, with efforts such as waste minimisation, implementation of alternative discharge methods, and radionuclide extraction systems gaining popularity; however, further evidence is needed to understand the economic and social impact of nuclear medicine procedures. The scoping review identified a range of strategies focused on mitigating the adverse impact on the environment and working towards efficient economic and social resources, to foster sustainable practices in nuclear medicine.

Artificial intelligence (AI) in positron emission tomography/computed tomography (PET/CT) can be used to improve image quality when it is useful to reduce the injected activity or the acquisition time. Particular attention must be paid to ensure that users adopt this technological innovation when outcomes can be improved by its use. The aim of this study was to identify the aspects that need to be analysed and discussed to implement an AI denoising PET/CT algorithm in clinical practice, based on the representations of Nuclear Medicine Technologists (NMT) from Western-Switzerland, highlighting the barriers and facilitators associated. Two focus groups were organised in June and September 2023, involving ten voluntary participants recruited from all types of medical imaging departments, forming a diverse sample of NMT. The interview guide followed the first stage of the revised model of Ottawa of Research Use. A content analysis was performed following the three-stage approach described by Wanlin. Ethics cleared the study. Clinical practice, workload, knowledge and resources were de 4 themes identified as necessary to be thought before implementing an AI denoising PET/CT algorithm by ten NMT participants (aged 31-60), not familiar with this AI tool. The main barriers to implement this algorithm included workflow challenges, resistance from professionals and lack of education; while the main facilitators were explanations and the availability of support to ask questions such as a "local champion". To implement a denoising algorithm in PET/CT, several aspects of clinical practice need to be thought to reduce the barriers to its implementation such as the procedures, the workload and the available resources. Participants emphasised also the importance of clear explanations, education, and support for successful implementation. To facilitate the implementation of AI tools in clinical practice, it is important to identify the barriers and propose strategies that can mitigate it.