Taming the Complexity: Using Artificial Intelligence in a Cross-Disciplinary Innovative Platform to Redefine Molecular Imaging and Radiopharmaceutical Therapy

Cardiovascular disease (CVD) is a leading cause of death, accounting for over 30% of annual global fatalities. Ischemic heart disease, in turn, is a frontrunner of worldwide CVD mortality. With the burden of coronary disease rapidly growing, understanding the nuances of cardiac imaging and risk prognostication becomes paramount. Myocardial perfusion imaging (MPI) is a frequently utilized and well established testing modality due to its significant clinical impact in disease diagnosis and risk assessment. Recently, nuclear cardiology has witnessed major advancements, driven by innovations in novel imaging technologies and improved understanding of cardiovascular pathophysiology. Applications of artificial intelligence (AI) to MPI have enhanced diagnostic accuracy, risk stratification, and therapeutic decision-making in patients with coronary artery disease (CAD). AI techniques such as machine learning (ML) and deep learning (DL) neural networks offer new interpretations of immense data fields, acquired through cardiovascular imaging modalities such as nuclear medicine (NM). Recently, AI algorithms have been employed to enhance image reconstruction, reduce noise, and assist in the interpretation of complex datasets. The rise of AI in nuclear medicine (AI-NM) has proven itself groundbreaking in the efficiency of image acquisition, post-processing time, diagnostic ability, consistency, and even in risk-stratification and outcome prognostication. To that end, this narrative review will explore these latest advances in AI in nuclear medicine and its rapid transformation of the cardiac diagnostics landscape. This paper will examine the evolution of AI-NM, review novel AI techniques and applications in nuclear cardiac imaging, summarize recent AI-NM clinical trials, and explore the technical and clinical challenges in its implementation of artificial intelligence.

Artificial intelligence (AI) holds great promise for advancing diagnostics and treatment in nuclear medicine. The rapid growth of AI over the past decade largely driven by advances in hardware components such as graphics processing units (GPUs) and the introduction of Deep Learning (DL) and convolutional neural networks (CNN). The integration of AI and medical imaging has the potential to revolutionize nuclear medicine by, e.g., accelerating image acquisition, enhancing image quality, enabling advanced image generation, assisting image interpretation, and aiding treatment planning. Clinical applications have been demonstrated for most medical specialties, including oncology, neurology and radionuclide therapy. The utilization of AI to provide automated, standardized procedures can help bring advanced imaging from major university centers to smaller local clinics, thus benefiting a broader range of patients. Additionally, AI has vast potential for predicting optimal treatment strategies, assessing risk, optimizing patient flow and outcome, and even improving productivity, but these capabilities have yet to be fully utilized. The fraction of clinical AI applications in general healthcare reaching beyond the prototyping phase are reported as low as 2% [1]. Indeed, in nuclear medicine very few AI developments have reached commercial maturity. Currently, most AI applications in nuclear medicine follow the imaging flow from image acquisition and reconstruction, post-processing and image preparation, image analysis, and decision support for clinical interpretation. Below we will briefly review selected areas and comment on challenges and opportunities for AI in nuclear medicine, with a special focus on the transition from development to clinical implementation.

We aimed to evaluate nuclear medicine professionals’ attitudes and perceptions of artificial intelligence (AI) in their field and medicine in general. We conducted a survey via the Tencent Questionnaire platform among nuclear medicine professionals at Zhongshan Hospital affiliated with Fudan University. The survey addressed aspects such as their understanding of AI applications in nuclear medicine and their perspectives on AI in this field and medicine in general. A total of 261 individuals, including 120 females and 141 males, responded to the questionnaire. Approximately 55.2% reported being acquainted with AI in nuclear medicine, whereas 32.2% indicated they did not have a basic understanding of the technologies used in these topics. The majority (96.2%) believed that AI could assist in identifying pathologies in nuclear medicine imaging, although 23.4% felt that it could not provide a definitive diagnosis. Most respondents agreed that AI would both revolutionize (66.7%) and enhance (94.2%) nuclear medicine. Furthermore, the majority (82.3%) disagreed with the notion that AI would replace human nuclear medicine physicians, while 92.3% supported the integration of AI into medical training. In subgroup analyses, male and tech-savvy respondents were more confident in the benefits of AI in nuclear medicine and less concerned about its potential negative impacts. Our regional study demonstrates a high level of acceptance of AI in nuclear medicine, with AI primarily regarded as a tool to enhance diagnostics and support clinical decision-making rather than to replace physicians. The findings underscore the importance of broader research as well as continuous education and ethical deliberations regarding the integration of AI into medical practice.

Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning

To examine current literature on integrating emerging technologies, artificial intelligence (AI), and informatics into medical imaging education. A systematic review of peer-reviewed literature published in the past 5 years was conducted, focusing on medical imaging education, radiography curricula, AI applications, and ethical considerations. Articles were analyzed to identify recurring themes and trends in implementing AI and informatics in medical imaging education programs. Four key themes emerged from the literature: integration of emerging technologies and AI in medical imaging education; foundational informatics concepts and emerging technologies essential for medical imaging professionals; clinical applications of AI in medical imaging practice; and ethical and professional considerations regarding AI adoption. Integrating AI and informatics into medical imaging education is increasingly recognized as essential, but curriculum constraints, faculty preparedness, and the evolving nature of AI technologies are challenges to integration. Ethical concerns, including bias in AI algorithms and the potential effect on professional decision-making, highlight the need for responsible implementation. International efforts to establish AI educational frameworks are emerging that emphasize the importance of scaffolding learning to gradually build competency. To ensure the safe and effective use of AI in medical imaging, structured education and professional training must be prioritized. Future research should explore best practices for AI and informatics curriculum development, standardized assessment of AI literacy, and long-term effects of AI on clinical decision-making. By addressing these areas, medical imaging professionals can remain at the forefront of technological advancements while maintaining ethical responsibility and patient-centered care.

Focus issue: Artificial intelligence in medical physics.

Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine

Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility. In recent years, the application of artificial intelligence (AI) in medical imaging has expanded rapidly. By incorporating voxel-level feature maps, the combination of radiomics and deep learning enables the extraction of rich textural, morphological, and microstructural features, while autonomously learning high-level abstract representations from clinical CT images, thereby revealing biological heterogeneity that is often imperceptible to conventional assessments. Leveraging these high-dimensional representations, AI models can provide more accurate predictions of nICT response. Future advancements in foundation models, multimodal integration, and dynamic temporal modeling are expected to further enhance the generalizability and clinical applicability of AI. AI-powered medical imaging is poised to support all stages of perioperative management in ESCC, playing a pivotal role in high-risk patient identification, dynamic monitoring of therapeutic response, and individualized treatment adjustment, thereby comprehensively advancing precision nICT.

Artificial intelligence (AI) has rapidly reshaped the global practice of nuclear medicine. Through this shift, the integration of AI into nuclear medicine education, clinical practice, and research has a significant impact on workforce diversity. While AI in nuclear medicine has the potential to be a powerful tool to improve clinical, research and educational practice, and to enhance patient care, careful examination of the impact of each AI tool needs to be undertaken with respect to the impact on, among other factors, diversity in the nuclear medicine workforce. Some AI tools can be used to specifically drive inclusivity and diversity of the workforce by supporting women and underrepresented minorities. Other tools, however, have the potential to negatively impact minority groups, leading to a widening of the diversity gap. This manuscript explores how various AI solutions have the potential to both negatively and positively affect diversity in the nuclear medicine workforce.

The emergence of artificial intelligence (AI) in nuclear medicine and radiology has been accompanied by AI commentators and experts predicting that AI would make radiologists, in particular, extinct. More realistic perspectives suggest significant changes will occur in medical practice. There is no escaping the disruptive technology associated with AI, neural networks, and deep learning, the most significant perhaps since the early days of Roentgen, Becquerel, and Curie. AI is an omen, but it need not be foreshadowing a negative event; rather, it is heralding great opportunity. The key to sustainability lies not in resisting AI but in having a deep understanding and exploiting the capabilities of AI in nuclear medicine while mastering those capabilities unique to the human resources.

The integration of Artificial Intelligence (AI) into nuclear medicine has transformed diagnostic and therapeutic processes, yet the opaque nature of many AI models hinders clinical adoption and trust. This narrative review aims to synthesize the current landscape of explainable AI (XAI) in nuclear medicine, emphasizing its role in enhancing transparency, bias mitigation, and regulatory compliance for robust clinical integration. Key chapters cover the fundamentals of XAI in nuclear medicine; XAI applications in PET and SPECT instrumentation and acquisition; image reconstruction; quantitative imaging and corrections; post-reconstruction processing and analysis; and radiotherapy. The review concludes with a discussion of challenges, limitations, and future directions, advocating for interdisciplinary advancements to bridge AI innovation with practical utility in patient care.

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 and use and biases are outlined, and appropriate use guidelines outlined 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.

Trustworthiness has become a key concept for the ethical development and application of artificial intelligence (AI) in medicine. Various guidelines have formulated key principles, such as fairness, robustness, and explainability, as essential components to achieve trustworthy AI. However, conceptualizations of trustworthy AI often emphasize technical requirements and computational solutions, frequently overlooking broader aspects of fairness and potential biases. These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. This viewpoint article presents an interdisciplinary approach to analyzing trust in AI and trustworthy AI within the medical context, focusing on (1) social sciences and humanities conceptualizations and legal perspectives on trust and (2) their implications for trustworthy AI in health care. It focuses on real-world challenges in medicine that are often underrepresented in theoretical discussions to propose a more practice-oriented understanding. Insights were gathered from an interdisciplinary workshop with experts from various disciplines involved in the development and application of medical AI, particularly in oncological imaging and genomics, complemented by theoretical approaches related to trust in AI. Results emphasize that, beyond common issues of bias and fairness, knowledge and human involvement are essential for trustworthy AI. Stakeholder engagement throughout the AI life cycle emerged as crucial, supporting a human- and multicentered framework for trustworthy AI implementation. Findings emphasize that trust in medical AI depends on providing meaningful, user-oriented information and balancing knowledge with acceptable uncertainty. Experts highlighted the importance of confidence in the tool's functionality, specifically that it performs as expected. Trustworthiness was shown to be not a feature but rather a relational process, involving humans, their expertise, and the broader social or institutional contexts in which AI tools operate. Trust is dynamic, shaped by interactions among individuals, technologies, and institutions, and ultimately centers on people rather than tools alone. Tools are evaluated based on reliability and credibility, yet trust fundamentally relies on human connections. The article underscores the development of AI tools that are not only technically sound but also ethically robust and broadly accepted by end users, contributing to more effective and equitable AI-mediated health care. Findings highlight that building AI trustworthiness in health care requires a human-centered, multistakeholder approach with diverse and inclusive engagement. To promote equity, we recommend that AI development teams involve all relevant stakeholders at every stage of the AI lifecycle—from conception, technical development, clinical validation, and real-world deployment.

UNSTRUCTURED Trustworthiness has become a key concept for the ethical development and application of artificial intelligence (AI) in medicine. Various guidelines have formulated key principles, such as fairness, robustness, and explainability, as essential components to achieve trustworthy AI. However, conceptualizations of trustworthy AI often emphasize technical requirements and computational solutions, frequently overlooking broader aspects of fairness and potential biases. These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. This viewpoint article presents an interdisciplinary approach to analyzing trust in AI and trustworthy AI within the medical context, focusing on (1) social sciences and humanities conceptualizations and legal perspectives on trust and (2) their implications for trustworthy AI in health care. It focuses on real-world challenges in medicine that are often underrepresented in theoretical discussions to propose a more practice-oriented understanding. Insights were gathered from an interdisciplinary workshop with experts from various disciplines involved in the development and application of medical AI, particularly in oncological imaging and genomics, complemented by theoretical approaches related to trust in AI. Results emphasize that, beyond common issues of bias and fairness, knowledge and human involvement are essential for trustworthy AI. Stakeholder engagement throughout the AI life cycle emerged as crucial, supporting a human- and multicentered framework for trustworthy AI implementation. Findings emphasize that trust in medical AI depends on providing meaningful, user-oriented information and balancing knowledge with acceptable uncertainty. Experts highlighted the importance of confidence in the tool's functionality, specifically that it performs as expected. Trustworthiness was shown to be not a feature but rather a relational process, involving humans, their expertise, and the broader social or institutional contexts in which AI tools operate. Trust is dynamic, shaped by interactions among individuals, technologies, and institutions, and ultimately centers on people rather than tools alone. Tools are evaluated based on reliability and credibility, yet trust fundamentally relies on human connections. The article underscores the development of AI tools that are not only technically sound but also ethically robust and broadly accepted by end users, contributing to more effective and equitable AI-mediated health care. Findings highlight that building AI trustworthiness in health care requires a human-centered, multistakeholder approach with diverse and inclusive engagement. To promote equity, we recommend that AI development teams involve all relevant stakeholders at every stage of the AI lifecycle—from conception, technical development, clinical validation, and real-world deployment.

The recent reinvigoration of theranostics comes with advances in computing technology, radiochemistry, and instrumentation that synergize with developments in artificial intelligence (AI). There is a wide array of applications of AI in nuclear medicine that have translational benefits to theranostics, including attenuation correction, artifact and noise reduction, enhanced workflow, and lesion characterization, and segmentation and quantitation, among many others. For theranostics, there are potentially significant applications that could move closer to precision medicine. Perhaps the most important application is predictive dosimetry from diagnostic images to optimize therapeutic dose. There are also valuable benefits from AI-augmented radioligand design and development, preclinical imaging, and practice sustainability. Generative AI has also emerged as a powerful tool to support decision-making, information dissemination, and medical image analysis. There are, however, several ongoing challenges that must be considered pertaining to the development and application of AI tools in theranostics.