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preLekára.sk / Journals / Nuclear Medicine / 2026 - 3

Artificial intelligence in nuclear medicine: ethical, legal and perspective challenges

Czech version

Authors: M. Bejtic 1,2;  V. Kamírová 1;  O. Lang 1;  M. Lang 1;  M. Darsa 2
Authors‘ workplace: Prague Medical Care Department, s. r. o., Praha 1;  Fakulta biomedicínského inženýrství, České vysoké učení technické v Praze, Kladno, ČR 2
Published in: NuklMed 2026;15:34-39
Category: Review Article

Overview

This article addresses broader considerations related to the use of artificial intelligence (AI) in nuclear medicine that extend beyond purely technical performance. It first focuses on the issue of AI trustworthiness and explainability (Explainable AI, XAI), emphasizing the importance of transparent and clinically interpretable outputs for both physicians and patients. The review then outlines key regulatory frameworks governing the deployment of AI in healthcare, including the requirements of the U.S. Food and Drug Administration (FDA) and European legislation, particularly the Medical Device Regulation (MDR), CE marking, and the forthcoming Artificial Intelligence Act. Ethical aspects are discussed, such as the risk of bias in training datasets leading to inequalities in healthcare, the protection of sensitive patient data during AI development, and the clarification of physician responsibility when AI is used as a decision-support tool. In addition, the article summarizes attitudes of healthcare professionals and patients toward AI based on published surveys, which reveal both optimism regarding its potential benefits and concerns about its impact on clinical practice. Finally, future perspectives of AI in nuclear medicine up to the year 2030 are considered, including anticipated technological advances and deeper integration into clinical workflows, alongside the continued need for human oversight and adherence to ethical principles. Overall, the article highlights that the successful implementation of AI in nuclear medicine requires not only robust technology, but also trust, regulatory clarity, and broad acceptance within the medical community.

Keywords:

bias – ethics – regulation – explainable AI – trustworthiness – AI in medicine

Sources

  1. Visvikis D, et al. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. Eur J Nucl Med Mol Imaging. 2022;49(13):4452-4463. https://doi.org/10.1007/s00259-022-05891-w

  2. Papadimitroulas P, et al. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Med. 2021;83 : 108-121. https://doi.org/10.1016/J.EJMP.2021.03.009

  3. de Vries BJL, et al. Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Front Med. 2023;10 : 1180773. https://doi.org/10.3389/fmed.2023.1180773

  4. Papandrianos N, et al. An explainable classification method of SPECT myocardial perfusion images in nuclear cardiology using deep learning and grad-CAM. Appl Sci. 2022;12(15):7592. https://doi.org/10.3390/app12157592

  5. Miller RJH, et al. Explainable deep learning improves physician interpretation of myocardial perfusion imaging. J Nucl Med. 2022;63(10):1768-1774. https://doi.org/10.2967/jnumed.121.263686

  6. Gevigney E, et al. Multimodal Fusion and Transfer Learning for the Detection of Degenerative Parkinsonisms with Dopamine Transporter SPECT Imaging. J Imaging Inform Med. 2026;39(1):1-15. https://doi.org/10.1007/s10278-025-01831-w

  7. Saboury B, et al. Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem. J Nucl Med. 2022;64(2):188-196. https://doi.org/10.2967/jnumed.121.263703

  8. Rahmim A, et al. Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine –⁠ The Bethesda Report (AI Summit 2022). arXiv. 2022. https://doi.org/10.48550/arxiv.2211.03783

  9. Buvat I, et al. The T.R.U.E. Checklist for Identifying Impactful Artificial Intelligence-Based Findings in Nuclear Medicine: Is It True? Is It Reproducible? Is It Useful? Is It Explainable? J Nucl Med. 2021;62(5):752-757. https://doi.org/10.2967/JNUMED.120.261586

  10. Luo Y, et al. Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation. Front Med. 2025;12 : 1728248. https://doi.org/10.3389/fmed.2025.1728248

  11. Sovrano F, et al. Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis. Front Artif Intell Appl. 2024;392 : 568-575. https://doi.org/10.3233/faia240568

  12. Venkadesh KV, et al. Ethical and Regulatory Dimensions of Integrating AI in Nuclear Medicine. In: Advances in Computational Intelligence and Robotics Book Series. IGI Global; 2025. https://doi.org/10.4018/979-8-3373-1275-0.ch001

  13. Selvakumar D, et al. Ethical and Regulatory Considerations of AI in Nuclear Medicine. In: Advances in Computational Intelligence and Robotics Book Series. IGI Global; 2025. https://doi.org/10.4018/979-8-3373-1275-0.ch002

  14. Huang Y, et al. Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach. Discov Oncol. 2025;16(1):3307. https://doi.org/10.1007/s12672-025-03307-3

  15. Kuhlen M, et al. Artificial Intelligence and Machine Learning in Pediatric Endocrine Tumors: Opportunities, Pitfalls, and a Roadmap for Trustworthy Clinical Translation. Biomedicines. 2026;14(1):146. https://doi.org/10.3390/biomedicines14010146

  16. Rahmim A, et al. Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine –⁠ The Bethesda Report (AI Summit 2022). 2022. Preprint.

  17. Jha AK, et al. Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE guidelines). J Nucl Med. 2022;63(9):1288-1299. https://doi.org/10.2967/jnumed.121.263239

  18. Herington J, et al. Ethical considerations for artificial intelligence in medical imaging: deployment and governance. J Nucl Med. 2023;64(10):1509-1516. https://doi.org/10.2967/jnumed.123.266110

  19. Currie G, et al. Ethical principles for the application of artificial intelligence (AI) in nuclear medicine. Eur J Nucl Med Mol Imaging. 2020;47(4):748-752. https://doi.org/10.1007/S00259-020-04678-1

  20. Currie G, et al. Ethical and legal challenges of artificial intelligence in nuclear medicine. Semin Nucl Med. 2021;51(2):120-125. https://doi.org/10.1053/J.SEMNUCLMED.2020.08.001

  21. Hustinx R, et al. An EANM position paper on the application of artificial intelligence in nuclear medicine. Eur J Nucl Med Mol Imaging. 2022;49(13):4369-4373. https://doi.org/10.1007/s00259-022-05947-x

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Nuclear medicine Radiodiagnostics Radiotherapy
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Persistent 99mTc-HDP uptake in a central venous catheter during bone scintigraphy
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Article was published in

Nuclear Medicine

Issue 3
2026 Issue 3
Most read in this issue
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