Utilizing artificial intelligence in nuclear medicine: Application and challenges

Abstract

The advent of artificial intelligence (AI) has been transformative across various domains, and nuclear medicine is no exception. The integration of AI into nuclear medicine has opened new avenues for enhancing diagnostic accuracy, personalizing treatment and optimizing workflows. However, this technological advancement is not without its challenges, which include technical, ethical and practical considerations that must be addressed to ensure AI's successful integration into clinical practice (Laudicella et al., 2023; Saboury et al., 2023). We read with great interest the recent article by Saban and colleagues entitled 'A comparative vignette study: Evaluating the potential role of a generative AI model in enhancing clinical decision-making in nursing' (Saban & Dubovi, 2024), which investigated the potential of a generative AI tool as clinical support for nurses. The findings of this study show that a generative AI tool demonstrated indecisiveness and a tendency towards over-triage compared to human clinicians. We are writing to express my interest and discuss the utilization of AI in nuclear medicine, focusing on its applications and challenges. Over the years, AI has been integrated into various medical fields, including nuclear medicine, to enhance diagnostic accuracy and improve patient outcomes (Jha et al., 2022; Saboury et al., 2023). AI's applications in nuclear medicine are vast, ranging from image enhancement to predictive analytics. In diagnostic imaging, AI algorithms have been developed to improve the quality of PET and SPECT images. These algorithms reduce noise and enhance spatial resolution, leading to more accurate diagnostics (Saboury et al., 2023). AI's ability to process and analyse large data sets allows for the development of predictive models that can assist in disease staging and prognosis. One of the significant advancements in AI is its application in PSMA PET/CT for prostate cancer imaging (Lindgren Belal et al., 2024). AI techniques, including machine learning and deep learning algorithms, have demonstrated the potential to match or even surpass human interpretation in detecting primary tumours, local recurrences and metastatic lesions (Saboury et al., 2023). These tools not only improve diagnostic accuracy but also reduce inter-reader variability and save valuable time. AI also plays a crucial role in the development of theranostics, where it aids in designing and optimizing radiopharmaceuticals for targeted therapy. By analysing large data sets of molecular interactions, AI can identify potential therapeutic targets and optimize the synthesis of radiopharmaceuticals. This capability enhances the precision and efficacy of nuclear medicine therapies, leading to better patient outcomes (Laudicella et al., 2023; Saboury et al., 2023). Despite its potential, the integration of AI in nuclear medicine presents several challenges. One of the primary technical challenges is the need for high-quality, curated data sets for training AI models. The availability of such data is often limited, hindering the development and validation of robust AI algorithms. Additionally, AI models require constant updates and retraining to incorporate new data and adapt to changing clinical practices (Laudicella et al., 2023). Another significant challenge is the ethical and legal implications of AI in health care. The use of AI in patient care raises concerns about data privacy, informed consent and the potential for algorithmic bias. These issues necessitate the establishment of clear regulatory frameworks and guidelines to ensure that AI technologies are used responsibly and ethically (Laudicella et al., 2023; Saban & Dubovi, 2024; Saboury et al., 2023). The implementation of AI in clinical workflows also faces practical barriers, such as resistance from healthcare providers and the need for significant investments in infrastructure and training. To overcome these challenges, it is essential to foster collaboration between AI developers, healthcare professionals and regulatory bodies to create a supportive ecosystem for AI innovation (Laudicella et al., 2023). Building a trustworthy AI ecosystem in nuclear medicine requires a multi-faceted approach (Jha et al., 2022; Laudicella et al., 2023; Saboury et al., 2023). First, it is crucial to establish standards for the development, evaluation and deployment of AI algorithms. This includes creating guidelines for data management, algorithm validation and post-deployment monitoring. Second, transparency and accountability must be prioritized throughout the AI lifecycle. Artificial intelligence developers should be transparent about the limitations and potential biases of their models, and mechanisms should be in place to hold developers accountable for the performance and outcomes of AI systems. Finally, education and training programs should be implemented to equip healthcare professionals with the skills and knowledge needed to effectively use AI tools. This will not only facilitate the adoption of AI technologies but also empower clinicians to critically evaluate and integrate AI insights into their practice. The future of AI in nuclear medicine is promising, with ongoing research aimed at enhancing the capabilities of AI systems and overcoming existing challenges. Key areas of focus include (Jha et al., 2022; Laudicella et al., 2023; Saboury et al., 2023) (1) Advanced algorithm development: Continued advancements in machine learning and deep learning techniques will enhance the accuracy and efficiency of AI tools in nuclear medicine. (2) Interdisciplinary collaboration: Collaboration between AI experts, clinicians and policymakers is essential to address the multifaceted challenges associated with AI implementation and to develop guidelines that ensure the safe and effective use of AI in healthcare. (3) Real-world implementation: Translating AI research into clinical practice requires pilot studies and real-world evaluations to assess the impact of AI tools on patient outcomes and healthcare systems. (4) Patient-centric approaches: Incorporating patient perspectives and preferences into the design and deployment of AI systems can enhance the relevance and acceptability of AI-driven healthcare solutions. AI has the potential to revolutionize nuclear medicine by enhancing diagnostic accuracy, personalizing treatment and optimizing clinical workflows. However, realizing this potential requires addressing the technical, ethical and practical challenges associated with AI integration. By fostering collaboration, establishing standards and promoting transparency, we can build a trustworthy AI ecosystem that enhances patient care and advances the field of nuclear medicine. Chong Cheng, Bin Tang and Pan Tang: Conceptualization, supervision, validation, writing—review and editing. Chong Cheng, Ping-Ping Li, Ling Zhang and Pan Tang: Conceptualization, validation, writing—review and editing. All authors agreed on the final version of the manuscript. None declared. The authors declare no conflicts of interest. Not applicable.

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