Generative Artificial Intelligence Biases, Limitations and Risks in Nuclear Medicine: An Argument for Appropriate Use Framework and Recommendations.

Generative artificial intelligence (AI) algorithms for both text-to-text and text-to-image applications have seen rapid and widespread adoption in the general and medical communities. While limitations of generative AI have been widely reported, there remain valuable applications in patient and professional communities. Here, the limitations and biases of both text-to-text and text-to-image generative AI are explored using purported applications in medical imaging as case examples. A direct comparison of the capabilities of four common text-to-image generative AI algorithms is reported and recommendations for the most appropriate use, DALL-E 3, justified. The risks use and biases are outlined, and appropriate use guidelines framed for use of generative AI in nuclear medicine. Generative AI text-to-text and text-to-image generation includes inherent biases, particularly gender and ethnicity, that could misrepresent nuclear medicine. The assimilation of generative AI tools into medical education, image interpretation, patient education, health promotion and marketing in nuclear medicine risks propagating errors and amplification of biases. Mitigation strategies should reside inside appropriate use criteria and minimum standards for quality and professionalism for the application of generative AI in nuclear medicine.

GPT-4 in Nuclear Medicine Education: Does It Outperform GPT-3.5?

The emergence of ChatGPT has challenged academic integrity in teaching institutions, including those providing nuclear medicine training. Although previous evaluations of ChatGPT have suggested a limited scope for academic writing, the March 2023 release of generative pretrained transformer (GPT)-4 promises enhanced capabilities that require evaluation. Methods: Examinations (final and calculation) and written assignments for nuclear medicine subjects were tested using GPT-3.5 and GPT-4. GPT-3.5 and GPT-4 responses were evaluated by Turnitin software for artificial intelligence scores, marked against standardized rubrics, and compared with the mean performance of student cohorts. Results: ChatGPT powered by GPT-3.5 performed poorly in calculation examinations (31.4%), compared with GPT-4 (59.1%). GPT-3.5 failed each of 3 written tasks (39.9%), whereas GPT-4 passed each task (56.3%). Conclusion: Although GPT-3.5 poses a minimal risk to academic integrity, its usefulness as a cheating tool can be significantly enhanced by GPT-4 but remains prone to hallucination and fabrication.

Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?

Academic integrity in both higher education and scientific writing has been challenged by developments in artificial intelligence. The limitations associated with algorithms have been largely overcome by the recently released ChatGPT; a chatbot powered by GPT-3.5 capable of producing accurate and human-like responses to questions in real-time. Despite the potential benefits, ChatGPT confronts significant limitations to its usefulness in nuclear medicine and radiology. Most notably, ChatGPT is prone to errors and fabrication of information which poses a risk to professionalism, ethics and integrity. These limitations simultaneously undermine the value of ChatGPT to the user by not producing outcomes at the expected standard. Nonetheless, there are a number of exciting applications of ChatGPT in nuclear medicine across education, clinical and research sectors. Assimilation of ChatGPT into practice requires redefining of norms, and re-engineering of information expectations.

The emerging role of artificial intelligence and digital twins in pre-clinical molecular imaging.

INTRODUCTION: Pre-clinical molecular imaging, particularly with mice, is an essential part of drug and radiopharmaceutical development. There remain ethical challenges to reduce, refine and replace animal imaging where possible. METHOD: A number of approaches have been adopted to reduce the use of mice including using algorithmic approaches to animal modelling. Digital twins have been used to create a virtual model of mice, however, exploring the potential of deep learning approaches to digital twin development may enhance capabilities and application in research. RESULTS: Generative adversarial networks produce generated images that sufficiently resemble reality that they could be adapted to create digital twins. Specific genetic mouse models have greater homogeneity making them more receptive to modelling and suitable specifically for digital twin simulation. CONCLUSION: There are numerous benefits of digital twins in pre-clinical imaging including improved outcomes, fewer animal studies, shorter development timelines and lower costs.

V/Q SPECT and SPECT/CT in Pulmonary Embolism.

Ventilation and perfusion (V/Q) lung scintigraphy has been used in the assessment of patients with suspected pulmonary embolism for more than 50 y. Advances in imaging technology make SPECT and SPECT/CT feasible. This article will examine the application and technical considerations associated with performing 3-dimensional V/Q SPECT and the contribution of a coacquired CT scan. The literature tends to be mixed and contradictory in terms of appropriate investigation algorithms for pulmonary embolism. V/Q SPECT and SPECT/CT offer significant advantages over planar V/Q, with or without the advantages of Technegas ventilation, and if available should be the preferred option in the evaluation of patients with suspected pulmonary embolism.

Emergency pharmacology for the CT technologist.

The scope of practice of the medical radiation practitioner demands knowledge and understanding of the indications, contraindications, warnings, precautions, proper use, drug interactions and adverse reactions of a variety of medications. The risk of patient deterioration or acute emergent event, particularly following contrast administration, makes the command of crash cart medications particularly important. This article explores the pharmacological principles of medications most likely to be required in a medical emergency in the medical radiation department and in particular by the computed tomography (CT) technologist. The article also outlines early warning signs to assist in identifying the emergent or deteriorating patient. The learning outlined is designed to equip medical radiation practitioners with the capacity to identify and respond to a medical emergency typical of the medical radiation department, and to respond to that situation with the appropriate use of emergency medications where appropriate. The ability of medical radiation practitioners to recognise and respond to (including the use of medicines) the deteriorating patient or circumstances of a medically urgent nature are key capabilities required to meet minimum standards for Medical Radiation Practice Board of Australia registration and National Safety and Quality Health Service standards.

Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.

Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment with scope to adjust dosing for optimal target and non-target tissue doses is consistent with striving for improved the outcomes of precision medicine. The combination of artificial intelligence and production of digital twins could provide an avenue for an individualised therapy doses and enhanced outcomes in theranostics. While there are barriers to overcome, the maturity of individual technologies (i.e. radiation dosimetry, artificial intelligence, theranostics and digital twins) places these approaches within reach.

Emotional Intelligence and Productive Relationships with Patients and Colleagues.

A higher degree of emotional intelligence among health professionals has been shown to result in better patient care and improved wellbeing of the health professional. For nuclear medicine, emotional competence of staff and emotional proficiency of institutions, are important expectations. Nonetheless, there is a paucity of material outlining purposeful honing of emotional intelligence, or the tools for such development, across the literature. While the hidden curriculum provides powerful and authentic educational opportunities, incidental or accidental (organic) capability development does not benefit overall professionalism. Deliberate curricula can be achieved through a scaffold of emotional training and immersion programs that allow the nuclear medicine student or practitioner to recognize and foster emotionally safe environments. This requires careful planning to drive the emotional intelligence pipeline. Central to this is an understanding of learning taxonomies. There remain substantial gaps between the most and least emotionally insightful that could be addressed by rich immersive activities targeting emotional proficiency among students and the graduate workforce.

Yindyamarra Winhanganha: A Conduit to Indigenous Cultural Proficiency.

The First Nations peoples in the United States, Canada, Australia, and around the world are substantially disadvantaged by colonialization, including health inequity. For nuclear medicine, the cultural competence of the staff and cultural proficiency of the institution are important minimum expectations. This minimum can be achieved through a scaffold of Indigenous cultural training and immersion programs that allow the nuclear medicine department to be a culturally safe environment for Indigenous patients. Development of such programs requires careful planning and inclusivity of Indigenous people as the key stakeholders but, done appropriately, can positively drive the Indigenous equity pipeline. Central to this undertaking is an understanding of Indigenous ways of learning and the nexus of these ways of learning and learning taxonomies. There remain substantial gaps between the most culturally insightful and the least culturally insightful (individuals and institutions)-gaps that can be addressed, in part, by rich immersive professional development activities in nuclear medicine targeting cultural proficiency and creating culturally safe clinical environments. The opportunity lies before us to provide leadership in nation building and in yindyamarra winhanganha: living respectfully while creating a world worth living in.

18F-DCFPyL PET/CT in Metastatic Renal Cell Carcinoma.

Targeted molecular imaging with PET uses chemical ligands that are peptides specifically targeting a receptor of interest. Prostate-specific membrane antigen (PSMA) is substantially upregulated in prostate cancer but is also expressed in the neovascular tissue of several malignancies, including renal cell carcinoma (RCC). Radiolabeled peptide targets for PSMA may be helpful in detecting metastatic RCC lesions. We present a case of incidental detection of RCC metastatic disease with PSMA-targeted PET, and we explore potential use for deliberate evaluation of RCC with PSMA-targeted tracers.

Re-Modelling 99m-Technetium Pertechnetate Thyroid Uptake; Statistical, Machine Learning and Deep Learning Approaches.

Background: While normal ranges for 99mTc thyroid percentage uptake vary, the seemingly intuitive evaluation of thyroid function does not reflect the complexity of thyroid pathology and biochemical status. The emergence of artificial intelligence (AI) in nuclear medicine has driven problem solving associated with logic and reasoning that warrant re-examination of established benchmarks in thyroid functional assessment. Methods: There were 123 patients retrospectively analysed in the study sample comparing scintigraphic findings to grounded truth established through biochemistry status. Conventional statistical approaches were used in conjunction with an artificial neural network (ANN) to determine predictors of thyroid function from data features. A convolutional neural network (CNN) was also used to extract features from the input tensor (images). Results: Analysis was confounded by sub-clinical hyperthyroidism, primary hypothyroidism, sub-clinical hypothyroidism and T3 toxicosis. Binary accuracy for identifying hyperthyroidism was highest for thyroid uptake classification using a threshold of 4.5% (82.6%), followed by pooled physician 6interpretation with the aid of uptake values (82.3%). Visual evaluation without quantitative values reduced accuracy to 61.0% for pooled physician determinations and 61.4% classifying on the basis of thyroid gland intensity relative to salivary glands. The machine learning (ML) algorithm produced 84.6% accuracy, however, this included biochemistry features not available to the semantic analysis. The deep learning (DL) algorithm had an accuracy of 80.5% based on image inputs alone. Conclusion: Thyroid scintigraphy is useful in identifying hyperthyroid patients suitable for radioiodine therapy when using an appropriately validated cut-off for the patient population (4.5% in this population). ML ANN algorithms can be developed to improve accuracy as second readers systems when biochemistry results are available. DL CNN algorithms can be developed to improve accuracy in the absence of biochemistry results. ML and DL do not displace the role of the physician in thyroid scintigraphy but could be used as second reader systems to minimize errors and increase confidence.

PET/MR Part 4: Clinical Applications of PET/MRI.

Position emission tomography (PET) and magnetic resonance imaging (MRI) as a hybrid modality provides novel imaging opportunities. While there are a very broad array of pathologies that could benefit from PET/MRI, there is only a narrow range of applications where benefit over standard care justifies the higher resource utilization and, in particular, offers a net positive trade-off over PET/CT. This benefit is generally associated with the omission of CT and the associated radiation dose from the patient workup. This manuscript provides a summary of the generally accepted clinical applications of PET/MRI in both adult and pediatric populations. While there are a number of potential applications and certainly exciting research that may expand applications in the future, the purpose of this paper was to focus on current, mainstream applications. This is the final manuscript in a four-part integrated series sponsored by the SNMMI-TS PET/MR Task Force in conjunction with the SNMMI-TS Publication Committee.

A Technical Overview of Technegas as a Lung Ventilation Agent.

Technegas is a carbon-based nanoparticle developed in Australia in 1984 and has been in widespread clinical use, including SPECT imaging, since 1986. Although 81mKr offers the ideal ventilation properties of a true gas, Technegas is considered preferred in more than 60 countries for ventilation imaging yet has limited adoption in the United States. In March 2020, a new U.S. Food and Drug Administration application was lodged for Technegas, and the impending approval warrants a detailed discussion of the technical aspects of the technology for those for whom it is new. Technegas is a simple yet versatile system for producing high-quality 99mTc-based ventilation studies. The design affords safety to patients and staff, including consideration of radiation and biologic risks. Technegas is the gold standard for the ventilation portion of SPECT-based ventilation-perfusion studies in pulmonary embolism and several respiratory pathologies. When approved by the U.S. Food and Drug Administration, Technegas will extend advantages to workflow, safety, and study quality for departments that adopt the technology.

PET/MRI, Part 2: Technologic Principles.

The challenges of hybridizing PET and MRI as a simultaneous modality have been largely overcome in recent times. PET hybridized with MRI has seen the emergence of PET/MRI systems in the clinical setting, and with it comes a responsibility to adapt appropriate facility design, safety practices, protocols and procedures, and clinical opportunity. This article provides an insight into the considerations and challenges associated with PET/MR technology. Given that the nature of PET is well established among the readership of this journal, the article provides an introduction to the foundations of MRI instrumentation and emphasis on specific technologic aspects of PET/MR systems. This article is the second 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, building on the previous article (part 1), which was on establishing a facility. In subsequent parts, PET/MRI will be explored on the basis of protocols and procedures (part 3) and applications and clinical cases (part 4).

PET/MRI, Part 1: Establishing a PET/MRI Facility.

The emergence of PET and MRI as a hybrid modality has generated widespread interest in the technology and techniques. Although adoption and use are unlikely to be as expansive as for PET and CT hybrid systems, PET/MRI is an important modality that requires broad insight for nuclear medicine professions generally and deeper insight for those engaged in PET/MRI practice. This article provides insight into the considerations and challenges associated with establishing a PET/MRI facility. Each clinical site will present unique requisites for establishing a PET/MRI facility, and indeed, each PET/MRI vendor will have specific site requirements. Nonetheless, this article provides general insight into common considerations but should not be considered exhaustive. Here, development of the facility, staffing of the facility, and implications of both radiation and MRI safety are considered from the context of facility design. Given that the nature of PET is well established among the readership of this journal, the article provides an emphasis on MRI factors. This article is the first 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. In the subsequent 3 parts, PET/MRI will be explored on the basis of technology principles (part 2), protocols and procedures (part 3), and applications and clinical cases (part 4).

Intelligent Imaging: Developing a Machine Learning Project.

Artificial intelligence (AI) has rapidly progressed, with exciting opportunities that drive enthusiasm for significant projects. A sensible and sustainable approach would be to start building an AI footprint with smaller, machine learning (ML)-based initiatives using artificial neural networks before progressing to more complex deep learning (DL) approaches using convolutional neural networks. Several strategies and examples of entry-level projects are outlined, including mock potential projects using convolutional neural networks toward which we can progress. The examples provide a narrow snapshot of potential applications designed to inspire readers to think outside the box at problem solving using AI and ML. The simple and resource-light ML approaches are ideal for problem solving, are accessible starting points for developing an institutional AI program, and provide solutions that can have a significant and immediate impact on practice. A logical approach would be to use ML to examine the problem and identify among the broader ML projects which problems are most likely to benefit from a DL approach.

A Lens on the Post-COVID-19 "New Normal" for Imaging Departments.

The COVID-19 pandemic has redefined the diagnostic imaging that is being practiced. It is important to consider how COVID-19 will reshape the practice in the post-COVID era. The "new normal" should reflect what has been learned from COVID-19 and preparedness for the future.

Topical Sensor for the Assessment of PET Dose Administration: Metric Performance with an Autoinjector.

Extravasation or partial extravasation of the radiopharmaceutical dose in PET can undermine SUV and image quality. A topical sensor has been validated using several metrics to characterize injection quality after manual injection. The performance of these metrics for autoinjector administration has been assessed. Methods: A single PET/CT scanner at a single site was used to characterize injections using an autoinjector with standardized apparatus, flush volume, and infusion rate (1-min infusion followed by 2 syringe flushes) for 18F-FDG, 68Ga-prostate-specific membrane antigen, and 68Ga-DOTATATE. In total, 296 patients with topical application of sensors were retrospectively analyzed using conventional statistical analysis and an artificial neural network. Results: Partial extravasation was noted in 1.3% of studies, with 9.1% (inclusive of partial extravasation) identified to have an injection anomaly (e.g., venous retention). Extravasation was independently predicted by the time that elapsed as the counts recorded by the injection sensor fell from the maximum value to within 200% of the reference sensor counts greater than 1,200 s; as the difference in counts for injection and reference sensors, normalized by dose, from 4 min after injection greater than 25; and as the ratio of the average counts per second recorded by the injection sensor at the end of a monitoring period to those of the reference sensor greater than 2. Conclusion: Extravasation and partial extravasation of PET doses are readily detected and differentiated using time-activity curve metrics. The metrics can provide the insight that could inform image quality or SUV accuracy issues. Further validation of key metrics is recommended in a larger and more diverse cohort.