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Background: Sustainability can be defined as the state in which consumption or depletion do not exceed regeneration. It can further be considered in five dimensions: environmental, economic, social, human resources, and ecological., Key Findings: There are a number of key issues that threaten sustainability across nuclear medicine clinical and research practices, and across the five dimensions of sustainability there is a requirement for compromise between conflicting priorities. Nonetheless, the field of nuclear medicine benefits from an inherent culture of innovation and forethought which fosters adaptation in order to achieve sustainability., Conclusion: The principles of sustainability are particularly challenging to navigate due to resource scarcity in nuclear medicine associated with both workforce shortages and supply disruptions. Specific challenges and adaptations are outlined for each of the five dimensions of sustainability., Implications for Practice: There are opportunities for improving sustainability of nuclear medicine practice although success is reliant on a deeper understanding of the interplay across the five dimensions of sustainability., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The GPT-3.5-powered ChatGPT was released in late November 2022 powered by the generative pretrained transformer (GPT) version 3.5. It has emerged as a readily accessible source of patient information ahead of medical procedures. Although ChatGPT has purported benefits for supporting patient education and information, actual capability has not been evaluated. Moreover, the March 2023 emergence of paid subscription access to GPT-4 promises further enhanced capabilities requiring evaluation. Methods: ChatGPT was used to generate patient information sheets suitable for gaining informed consent for 7 common procedures in nuclear medicine. Responses were generated independently for both GPT-3.5 and GPT-4 architectures. Specific procedures were selected that had a long-standing history of use to avoid any bias associated with the September 2021 learning cutoff that constrains both GPT-3.5 and GPT-4 architectures. Each information sheet was independently evaluated by 3 expert assessors and ranked on the basis of accuracy, appropriateness, currency, and fitness for purpose. Results: ChatGPT powered by GPT-3.5 provided patient information that was appropriate in terms of being patient-facing but lacked accuracy and currency and omitted important information. GPT-3.5 produced patient information deemed not fit for the purpose. GPT-4 provided patient information enhanced across appropriateness, accuracy, and currency, despite some omission of information. GPT-4 produced patient information that was largely fit for the purpose. Conclusion: Although ChatGPT powered by GPT-3.5 is accessible and provides plausible patient information, inaccuracies and omissions present a risk to patients and informed consent. Conversely, GPT-4 is more accurate and fit for the purpose but, at the time of writing, was available only through a paid subscription., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

ChatGPT chatbot powered by GPT 3.5 was released in late November 2022 but has been rapidly assimilated into educational and clinical environments. Method: Insight into ChatGPT capabilities was undertaken in an interview-style approach with the chatbot itself. Results: ChatGPT powered by GPT 3.5 exudes confidence in its capabilities in supporting and enhancing student learning in nuclear medicine and in supporting clinical practice. ChatGPT is also self-aware of limitations and flaws in capabilities and the risks these pose to academic integrity. Conclusion: Further objective evaluation of ChatGPT capabilities in authentic learning and clinical scenarios is required., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Academic integrity has been challenged by artificial intelligence algorithms in teaching institutions, including those providing nuclear medicine training. The GPT 3.5-powered ChatGPT chatbot released in late November 2022 has emerged as an immediate threat to academic and scientific writing. Methods: Both examinations and written assignments for nuclear medicine courses were tested using ChatGPT. Included was a mix of core theory subjects offered in the second and third years of the nuclear medicine science course. Long-answer-style questions (8 subjects) and calculation-style questions (2 subjects) were included for examinations. ChatGPT was also used to produce responses to authentic writing tasks (6 subjects). ChatGPT responses were evaluated by Turnitin plagiarism-detection software for similarity and artificial intelligence scores, scored against standardized rubrics, and compared with the mean performance of student cohorts. Results: ChatGPT powered by GPT 3.5 performed poorly in the 2 calculation examinations (overall, 31.7% compared with 67.3% for students), with particularly poor performance in complex-style questions. ChatGPT failed each of 6 written tasks (overall, 38.9% compared with 67.2% for students), with worsening performance corresponding to increasing writing and research expectations in the third year. In the 8 examinations, ChatGPT performed better than students for general or early subjects but poorly for advanced and specific subjects (overall, 51% compared with 57.4% for students). Conclusion: Although ChatGPT poses a risk to academic integrity, its usefulness as a cheating tool can be constrained by higher-order taxonomies. Unfortunately, the constraints to higher-order learning and skill development also undermine potential applications of ChatGPT for enhancing learning. There are several potential applications of ChatGPT for teaching nuclear medicine students., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Introduction: The identification of unknown radionuclides is an authentic practical activity for students that provides the foundations for clinical problem solving, especially in the storage and management of radioactive waste. As different radionuclides have different half-lives, some of which are quite long, the storage of waste material has to accommodate the longest of these. Cross contamination requires a method of identifying the radionuclide samples in a mixed sample to safely and appropriately manage disposal. Similarly, identifying a single unknown sample of a radionuclide allows correct handling and disposal., Methods: Performing a systematic investigation of the physical properties of unknown radioactive samples is a rich learning opportunity to instil understanding of important physics principles among students in nuclear medicine., Results: This manuscript outlines an investigation developed that would allow students to identify single unknown radionuclides based on physical properties and identify the constituent radionuclides of a mixed sample using some additional mathematical curve stripping., Conclusion: The processes and solutions are provided with real data and this practical activity can be replicated by students generating their own data., Implications for Practice: This paper provides a template and analysis/interpretation guideline for educators and clinicians to deepen understanding of foundation physics. Enhanced and deeper understanding are a vehicle for improved problem solving in clinical and research practice., Competing Interests: Conflict of interest statement None., (Copyright © 2022 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)

CE credit : For CE credit, you can access the test for this article, as well as additional JNMT CE tests, online at https://www.snmmilearningcenter.org Complete the test online no later than March 2025. Your online test will be scored immediately. You may make 3 attempts to pass the test and must answer 75% of the questions correctly to receive Continuing Education Hour (CEH) credit. Credit amounts can be found in the SNMMI Learning Center Activity. SNMMI members will have their CEH credit added to their VOICE transcript automatically; nonmembers will be able to print out a CE certificate upon successfully completing the test. The online test is free to SNMMI members; nonmembers must pay $15.00 by credit card when logging onto the website to take the test.The emergence of PET and MRI as a hybrid modality has demanded new approaches to protocols and procedures. Although protocols for MRI and PET individually lend themselves to synergistic and simultaneous approaches, there are a number of unique challenges and patient preparations that require consideration. This article provides insight into the protocols, procedures, and challenges associated with simultaneous PET/MRI in both adult and pediatric populations. Although protocols may be specific to applications or pathologies of interest, a richer discussion of the clinical applications of PET/MRI is beyond the scope of this article and will be detailed in part 4 of the series. The foundation of PET/MRI protocols is an understanding of the various MRI sequences, which are outlined succinctly. The principles outlined for protocols and procedures are general, and specific application will vary among departments. Given that the procedures for PET are well established among the readership of this journal, this article emphasizes MRI factors unless specific variations in standard PET protocols or procedures are driven by the simultaneous MRI. This article is the third in a 4-part integrated series sponsored by the PET/MR and Publication Committees of the Society of Nuclear Medicine and Molecular Imaging-Technologist Section., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Objectives: Growing interest in the applications of artificial intelligence (AI) and, in particular, deep learning (DL) in nuclear medicine and radiology partitions the professional community. At one end of the spectrum are our expert DL wizards developing potion-like code and waving the DL capabilities like a wand across our professions. On the opposite side of the spectrum are our muggle colleagues who lack the wizardry of DL and may be largely oblivious to the entire magical realm., Key Findings: As crafted by Arthur C Clark, any sufficiently advanced technology is indistinguishable from magic. DL is not only an important technology in the future of medical imaging, but its application lives in the capabilities of medical imaging technologists. This may be incidental through application of techniques at the patient interface, through role expansion in data curation and management, or as active members of DL projects and development. Understanding the rudimentary principles of DL is emerging as requisite in medical imaging., Conclusion: AI and DL are valuable tools in advancing capabilities and outcomes in medical imaging. A working knowledge of the technology and techniques is important and achievable for the medical imaging technologist even when capability in application of DL to research and clinical practice is not within one's interests or scope of practice., Implications for Practice: While there is no requisite for all of the professional community to be tutored in the wizardry of DL, there are benefits for the profession and our patients for all to have a rudimentary understanding of the language and landscape. The breadth of DL literature assumes a level of understanding not evident for the bulk of our professions. This manuscript provides a simplified primer on DL with the aim of arming the muggles among us with sufficient insight to navigate the magical realm of DL without transferring any wizardry capability itself., Competing Interests: Conflict of interest statement There are no funding or conflicts of interest to declare., (Copyright © 2021 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)

The emergence of artificial intelligence (AI) in nuclear medicine has occurred over the last 50 years but more recent developments in machine learning (ML) and deep learning (DL) have driven new capabilities of AI in nuclear medicine. In nuclear medicine, the artificial neural network (ANN) is the backbone of ML and DL. The inputs may be radiomic features that have been extracted from the image files or, if using a convolutional neural network (CNN), may be the images themselves. AI in nuclear medicine re-engineers and re-imagines clinical and research capabilities. An understanding of the principles of AI, ML and DL contextualised to nuclear medicine allows richer engagement in clinical and research applications, and capacity for problem solving where required. Simple applications of ML include quality assurance, risk assessment, business analytics and rudimentary classifications. More complex applications of DL for detection, localisation, classification, segmentation, quantitation and radiomic feature extraction using CNNs can be applied to general nuclear medicine, SPECT, PET, CT and MRI. There are also applications of ANNs and ML that allow small datasets (and larger ones) to be analysed in parallel to conventional statistical analysis. AI has assimilated into the clinical and research practice of nuclear medicine with little disruption. The emergence of ML and DL applications, however, has produced a seismic significant shift in the clinical and research landscape that demands at least rudimentary understanding of the principles of AI, ANNs and CNNs among nuclear medicine professionals., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) in nuclear medicine has gained significant traction and promises to be a disruptive, but innovative, technology. Recent developments in artificial neural networks, machine learning, and deep learning have ignited debate with respect to ethical and legal challenges associated with the use of AI in healthcare and medicine. While AI in nuclear medicine has the potential to improve workflow and productivity, and enhance clinical and research capabilities, there remains a professional responsibility to the profession and to patients: ethical, social, and legal. Enthusiasm to embrace new technology should not displace responsibilities for the ethical, social, and legal application of technology. This is especially true in relation to data usage, the algorithms applied, and how algorithms are used in practice. Governance of software and algorithms used for detection (segmentation) and/or diagnosis (classification) of disease using medical images requires rigorous evidence-based regulation. A number of frameworks have been developed for ethical application of AI generally in society and in radiology. For nuclear medicine, consideration needs to be given to beneficence, nonmaleficence, fairness and justice, safety, reliability, data security, privacy and confidentiality, mitigation of bias, transparency, explainability, and autonomy. AI is merely a tool, how it is utilised is a human choice. There is potential for AI applications to enhance clinical and research practice in nuclear medicine and concurrently produce deeper, more meaningful interactions between the physicians and the patient. Nonetheless ethical, legal, and social challenges demand careful attention and formulation of standards/guidelines for nuclear medicine., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Unlabelled: Publications form the knowledge base of any profession. Patterns in professional publications provide insight into the profession's maturity and global status. To our knowledge, publication productivity in nuclear medicine technology has not been reported. A recent study on publication productivity in radiography and radiation therapy provided interesting insight; however, a sampling bias resulted in study flaws., Methods: The most productive medical radiation technologists were determined by collecting data from 7 key, international peer-reviewed journals for the medical radiation sciences over a 5-y period. A full list of the technologists' publications, for the 5-y period, was obtained using a PubMed and ResearchGate search, and the authors were analyzed., Results: In total, 165 medical radiation technologists were identified who had published 3 or more articles between 2009 and 2013. Of these authors, 55.2% (91/165) were radiographers, 35.2% (58/165) were radiation therapists, and 9.6% (16/165) were nuclear medicine technologists. Overall, the majority of the most prolific authors were academics (104/165; 63.0%). After we applied a correction factor (the productivity per member of the registered workforce), radiography had the fewest authors publishing, compared with the relative workflow sizes., Conclusion: Nuclear medicine technologists demonstrated a high degree of productivity both absolutely and relatively. Consequently, nuclear medicine technologists have a productive research culture and command a large footprint within and outside the key medical radiation science journals., (© 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)