KEY WORDS: Artificial Intelligence (AI), Theranostics, Dosimetry, Radiopharmaceutical Therapy (RPT), Patient-friendly dosimetry KEY POINTS - The rapid evolution of radiopharmaceutical therapy (RPT) highlights the growing need for personalized and patient-centered dosimetry. - Artificial Intelligence (AI) offers solutions to the key limitations in current dosimetry calculations. - The main advances on AI for simplified dosimetry toward patient-friendly RPT are reviewed. - Future directions on the role of AI in RPT dosimetry are discussed.

India faces a significant cancer burden, with an incidence - to - mortality ratio indicating that nearly three out of five individuals diagnosed with cancer succumb to the disease. While the limitations of physical healthcare infrastructure are widely acknowledged as a primary challenge, concerted efforts by government and healthcare agencies are underway to mitigate these constraints. However, given the country's vast geography and high population density, it is imperative to explore alternative soft infrastructure solutions to complement existing frameworks . Artificial Intelligence agents are increasingly transforming problem - solving approaches across various domains, with their application in medicine proving particularly transformative. In this perspective, we examine the potential role of AI agents in advancing nuclear medicine fo r cancer research, diagnosis, and management in India. We begin with a brief overview of AI agents and their capabilities, followed by a proposed agent - based ecosystem that can address prevailing sustainability challenges in India's nuclear medicine. Keywords: AI Agents; cancer; nuclear medicine ecosystem; sustainability challenges 1. Introduction India's with population of 1.4 billion faces a significant cancer burden, with ~1.5 million new cases and ~850,000 deaths annually [1] [2] . With an i ncidence - to - m ortality p ercentage of approximately 64.8%, nearly three out of five individuals diagnosed with cancer are expected to succumb to the disease [2] . Projections indicate that mortality rates will rise significantly, increasing from 64.7% to 109.6% between 2022 and 2050, largely due to demographic shifts as the reproductive - age population transitions into middle and old age. This growing cancer burden will place even more pressure on the already overburdened healthcare system, making it essential to address the gaps in both infrastructure and indigenous research and innovations to ensure timely and effective patient treatment [3] . This trend underscores the urgent need for a resilient, patient - centred framework that integrates medical advancements, early detection through diagnostics, timely therapeutic interventions, and equitable access to care. Nuclear medicine uses a small amount of targeted radioactive material to diagnose and treat cancer [4] .

PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceuticals, confounding differentiation from malignant tumors. Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tumor processes. Nevertheless, the hour-long distribution period of radiopharmaceuticals post-injection complicates the determination of optimal timing for the second scan, presenting challenges in both practical applications and research. Notably, we have identified that delay time PET imaging can be framed as an image-to-image conversion problem. Motivated by this insight, we propose a novel spatial-temporal guided diffusion transformer probabilistic model (st-DTPM) to solve dual-time PET imaging prediction problem. Specifically, this architecture leverages the U-net framework that integrates patch-wise features of CNN and pixel-wise relevance of Transformer to obtain local and global information. And then employs a conditional DDPM model for image synthesis. Furthermore, on spatial condition, we concatenate early scan PET images and noisy PET images on every denoising step to guide the spatial distribution of denoising sampling. On temporal condition, we convert diffusion time steps and delay time to a universal time vector, then embed it to each layer of model architecture to further improve the accuracy of predictions. Experimental results demonstrated the superiority of our method over alternative approaches in preserving image quality and structural information, thereby affirming its efficacy in predictive task.

The escalating global cancer burden underscores the critical need for precise diagnostic tools in oncology. This research employs deep learning to enhance lesion segmentation in PET/CT imaging, utilizing a dataset of 900 whole-body FDG-PET/CT and 600 PSMA-PET/CT studies from the AutoPET challenge III. Our methodical approach includes robust preprocessing and data augmentation techniques to ensure model robustness and generalizability. We investigate the influence of non-zero normalization and modifications to the data augmentation pipeline, such as the introduction of RandGaussianSharpen and adjustments to the Gamma transform parameter. This study aims to contribute to the standardization of preprocessing and augmentation strategies in PET/CT imaging, potentially improving the diagnostic accuracy and the personalized management of cancer patients. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/DC2024.

This study explores a workflow for automated segmentation of lesions in FDG and PSMA PET/CT images. Due to the substantial differences in image characteristics between FDG and PSMA, specialized preprocessing steps are required. Utilizing YOLOv8 for data classification, the FDG and PSMA images are preprocessed separately before feeding them into the segmentation models, aiming to improve lesion segmentation accuracy. The study focuses on evaluating the performance of automated segmentation workflow for multitracer PET images. The findings are expected to provide critical insights for enhancing diagnostic workflows and patient-specific treatment plans.

Arman Rahmim Departments of Radiology and Physics, University of British Columbia Tyler J. Bradshaw Department of Radiology, University of Wisconsin Guido Davidzon Department of Radiology, Division of Nuclear Medicine & Molecular Imaging, Stanford University Joyita Dutta Department of Biomedical Engineering, University of Massachusetts Amherst Georges El Fakhri PET Center, Departments of Radiology & Biomedical Engineering and Bioinformatics & Data Science, Yale University Munir Ghesani United Theranostics Nicolas A. Karakatsanis Department of Radiology, Weill Cornell Medical College of Cornell University, New York Quanzheng Li Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School Chi Liu Department of Radiology and Biomedical Imaging, Yale University Emilie Roncali Departments of Biomedical Engineering and Radiology, University of California, Davis Babak Saboury Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health Tahir Yusufaly Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine Abhinav K. Jha Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University, St. Louis Abstract The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was "AI in Action". Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript. Introduction The Society of Nuclear Medicine & Molecular Imaging (SNMMI) 2nd Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Over 100 community members and stakeholders from academia, healthcare, industry, and NIH gathered to discuss the emerging role of AI in nuclear medicine. It featured two plenaries, panel discussions, talks from leading experts in the field, and was concluded by a round table discussion on key findings, next steps, and call to action.

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Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of healthcare. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a roadmap for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technological revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy and workflow efficiency, as well as emerging challenges and critical responsibilities are discussed. Establishing and maintaining leadership in AI requires a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, referring providers, among other stakeholders, while protecting our patients and society. This strategic plan is prepared by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging (SNMMI).

Arman Rahmim Departments of Radiology and Physics, University of British Columbia Tyler J. Bradshaw Department of Radiology, University of Wisconsin - Madison Irène Buvat Institut Curie, Université PSL, Inserm, Université Paris-Saclay, Orsay, France Joyita Dutta Department of Biomedical Engineering, University of Massachusetts Amherst Abhinav K. Jha Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis Paul E. Kinahan Department of Radiology, University of Washington Quanzheng Li Department of Radiology, Massachusetts General Hospital and Harvard Medical School Chi Liu Department of Radiology and Biomedical Imaging, Yale University Melissa D. McCradden Department of Bioethics, The Hospital for Sick Children, Toronto Babak Saboury Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health Eliot Siegel Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, USA John J. Sunderland Departments of Radiology and Physics, University of Iowa Richard L. Wahl Mallinckrodt Institute of Radiology, Washington University in St. Louis Abstract The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized. Introduction The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. As summarized in Figure 1, various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA) participated in the AI Summit; and the meeting included rich presentations, roundtable discussion and interactions on key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine.

But 2021 will likely be a big year for A.I., and with a new White House administration soon in place, there may be a clearer set of national A.I. policies that will trickle down to the business world. On New Year's Day, the U.S. Senate voted to overturn President Trump's veto of the National Defense Authorization Act and authorize $741 billion for defense spending, including the creation of a number of A.I.-related polices. Among the reasons Trump opposed the defense bill was the absence of a provision to repeal Section 230, which gives legal protections to Internet companies that host user-generated content. Although the defense bill was mostly geared toward military spending, it did contain a number of non-defense related A.I. initiatives, as Stanford University's Human-Centered Artificial Intelligence group outlined. For instance, the bill would create a "National AI Initiative" that would coordinate A.I. research and development between "civilian agencies," the Defense Department, and intelligence agencies.