Nuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024)

2024-06-03

AI Summary

Clinical translation of AI in nuclear medicine remains hindered by the absence of standardized value assessment frameworks, insufficient data and model sharing, unclear reimbursement mechanisms, and regulatory uncertainty. Method: This study systematically synthesizes key outcomes from the 2024 SNMMI Artificial Intelligence Summit and introduces— for the first time—the Nuclear Medicine AI Value Assessment Framework and a cross-institutional collaborative governance pathway. It integrates large language models, generative AI, medical data standardization (e.g., DICOM-SR, FHIR), open-source AI toolkits (e.g., MONAI), and real-world evidence analytics. Contributions: (1) A consensus-driven six-domain implementation roadmap; (2) Launch of a multi-center data-sharing initiative and prospective clinical validation pilots; (3) Provision of critical evidence to inform FDA and CMS policies on AI regulation and reimbursement; and (4) Advancement of dual-track deployment—toward clinically reimbursable applications and a sustainable open-science ecosystem.

Technology Category

• Philosophy and Ethics of AI: Morality & Value-based AI
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Advancing AI applications in nuclear medicine practice
• Developing computational tools for nuclear oncology needs
• Establishing value proposition and infrastructure for AI integration

Innovation

• AI Task Force efforts and ongoing initiatives
• Computational nuclear oncology tools development
• Large language and generative models exploration