Nuclear medicine is undergoing rapid expansion as healthcare systems adopt advanced imaging technologies, AI-enabled workflows, and new radiopharmaceuticals to deliver personalized diagnostics and targeted therapies. The field is transitioning from early-stage innovation to large-scale clinical implementation, driven by growing demand for precision care in cardiology, neurology, and oncology. The global nuclear medicine market is projected to grow from approximately $7.8 billion in 2024 to more than $30.7 billion by 2034, reflecting this momentum.

For decades, nuclear medicine remained largely confined to specialized hospital departments. But advances in imaging systems, radiopharmaceuticals, and artificial intelligence are changing that equation. Healthcare systems are now scaling nuclear medicine operations to reach patients in community settings and outpatient clinics, while AI-powered software helps clinicians make faster, more confident diagnostic decisions.

What's Driving the Shift From Hospital-Only to Community-Based Nuclear Medicine?

The expansion is fueled by several converging factors. Rising prevalence of chronic diseases is driving demand for timely, advanced diagnostics. Simultaneously, new care delivery models are emerging that leverage ready-to-use radiopharmaceuticals and mobile imaging units to bring precision diagnostics beyond traditional hospital walls. In cardiology, for example, GE HealthCare's Flyrcado, a PET imaging agent for myocardial perfusion imaging, is designed as a unit-dose product that simplifies workflows in community settings. Mobile imaging models are also extending access to sites that may not otherwise have the infrastructure for advanced nuclear medicine.

Theranostics, a combined diagnostic and therapeutic approach, is accelerating this shift. As adoption grows, healthcare systems are seeking standardized approaches to imaging, treatment planning, and response assessment. This demand is creating pressure to scale operations while maintaining diagnostic confidence and workflow efficiency.

"Nuclear medicine is moving from early innovation to large-scale clinical implementation. Healthcare systems need technologies and workflows that help expand access, improve efficiency and support more confident clinical decision-making as theranostics adoption and precision care evolve," said Jean-Luc Procaccini, President and CEO of Molecular Imaging and Computed Tomography at GE HealthCare.

Jean-Luc Procaccini, President and CEO, Molecular Imaging and Computed Tomography, GE HealthCare

How Are AI and Advanced Software Improving Nuclear Medicine Workflows?

Artificial intelligence is playing a central role in making nuclear medicine more scalable and efficient. AI-powered software tools are automating time-consuming tasks, reducing manual analysis, and enabling quantitative insights that support clinical decision-making. Several key technologies are emerging:

• Automated Tumor Analysis: MIM LesionID Pro, recently cleared by the U.S. FDA, uses AI to automate whole-body tumor burden analysis, eliminating the need for manual lesion segmentation and multi-image registration while providing fast access to quantitative insights.
• Dynamic Imaging and Kinetic Modeling: MIM KineticID, a 510(k)-pending software tool, enables dynamic PET imaging and kinetic modeling to support time-based analysis of radiotracer behavior, helping clinicians and researchers make more informed decisions.
• Deep Learning Image Processing: GE HealthCare's Omni Legend PET/CT platform includes Precision DL deep learning image processing, which improves image quality and enhances lesion detectability, supporting faster scans and better diagnostic confidence.

These tools are designed to streamline workflows and support more consistent clinical decision-making across care settings. In oncology, for instance, AI-enabled platforms help simplify whole-body tumor burden assessment and support standardized treatment planning. In neurology, quantitative tools like MIMneuro help providers integrate amyloid PET imaging more efficiently into routine care pathways for Alzheimer's diagnostics.

Which Medical Specialties Are Seeing the Most Growth?

Three clinical areas are leading the expansion of nuclear medicine. In cardiology, ready-to-use PET imaging agents are making myocardial perfusion imaging routine in community settings, extending access beyond traditional hospital environments. In neurology, growing demand for Alzheimer's diagnostics is driving adoption of amyloid PET imaging, supported by quantitative analysis tools that improve consistency and clinical decision-making. In oncology, theranostics adoption is accelerating, with healthcare systems seeking standardized approaches to imaging, treatment planning, and response assessment.

"This is an exciting moment for nuclear medicine because we are moving beyond simply detecting disease to truly understanding its biology and behavior. Technologies that combine advanced imaging, quantitative analysis and innovative radiopharmaceuticals are helping clinicians make more informed decisions earlier in the care pathway, ultimately improving how we diagnose, treat and monitor patients," noted Dr. Munir Ghesani, Chief Medical Officer at United Theranostics and System Chief of Nuclear Medicine at the Mount Sinai Health System in New York.

Dr. Munir Ghesani, Chief Medical Officer, United Theranostics and System Chief of Nuclear Medicine, Mount Sinai Health System

What Role Does AI Play in Pain Management and Chronic Condition Care?

Beyond imaging, artificial intelligence is also transforming how clinicians diagnose and manage complex conditions like chronic pain. Pain is a multidimensional experience involving biological, psychological, and social factors, making it a compelling yet technically challenging target for AI applications. Machine learning models are now being used to predict treatment response, identify novel pain phenotypes, and support personalized management strategies.

In one notable example, researchers developed machine learning models that integrated spinal imaging data with clinical information to predict response to spinal cord stimulation. In a large U.S. cohort, these models achieved 90% accuracy in identifying patients who would experience at least 50% pain relief, with an area under the curve of 91.4%. These results suggest that AI can help clinicians identify which patients are most likely to benefit from specific interventions before treatment begins.

However, experts emphasize that AI should be viewed as a decision-support tool rather than a diagnostic replacement. Pain is complex because structural findings from imaging do not always correlate with symptoms, and psychosocial factors play a major role in outcomes. In a randomized trial comparing AI-delivered cognitive behavioral therapy for chronic pain with standard therapy delivered by a human therapist, AI was found to be noninferior and potentially superior, while also improving access and reducing therapist resource demands.

What Challenges Remain as Nuclear Medicine Scales?

Despite the momentum, healthcare systems face operational challenges as they scale nuclear medicine. These include expanding infrastructure, streamlining workflows, and extending patient access beyond traditional hospital settings. Additionally, the field must develop objective markers for pain and other subjective conditions, as current approaches rely heavily on patient self-report and clinical judgment.

Researchers are working to develop machine learning models that can objectively classify pain levels using neuroimaging and heart rate metrics. Additional studies are examining whether AI systems can predict which patients will respond best to specific medications, nonpharmacologic treatments like acupuncture, or even placebo, based on initial intake interviews. Wearable sensors, including watches, wristbands, and patch-based devices, are enabling continuous monitoring of physiologic signals, providing objective insights into pain response patterns and supporting digital phenotyping integrated into AI-driven frameworks.

As nuclear medicine adoption continues to accelerate, the convergence of advanced imaging, AI-powered software, and innovative radiopharmaceuticals is reshaping how clinicians diagnose, treat, and monitor patients across multiple specialties. The next decade will likely see nuclear medicine become as routine in community settings as it is in hospitals today.