Article Figures & Data
Figures
- FIGURE 1.
New technologies in medicine have coincided with each phase of industrial revolution. First industrial revolution was mechanization, with mechanical loom invented in 1784. The stethoscope was invented by René Laennec in 1816 and improved by Arthur Leared (1851) and George Philip Cammann (1852). Second industrial revolution was driven by advent of electricity, with the commercial light bulb (patented by Thomas Edison in 1879), telegram, and modern factory production line. Electrocardiogram was invented by Augustus Waller in 1887 by projecting the heartbeat captured by Lippmann capillary electrometer onto photographic plate, allowing heartbeat to be recorded in real time. Willem Einthoven (1895) assigned letters P, Q, R, S, and T to the theoretic waveform. Third industrial revolution, known as digital revolution, brought computing technology and refined it to personal computer. In 1960s, Kuhl and Edwards developed cross-sectional CT and implemented this in the SPECT scanner, which was later applied to CT scanner by Sir Godfrey Hounsfield and Allan Cormack in 1972. Fourth industrial revolution is that of modern day, with big data, hyperconnectivity, and neural networks, resulting in ability to propel self-driving cars and development of AI in nuclear medicine. CNN = convolutional neural network; IoT = Internet of things.
- FIGURE 2.
From patient to image creation and back to physician, there are opportunities for AI systems to act at nearly any step in medical imaging pipeline to improve our ability to care for patients and understand disease (3).
- FIGURE 3.
Dosimetry as major frontier supported by AI toward personalization of therapy: various contributions by AI to image acquisition, generation, and processing, followed by automated dose calculations, can enable routine deployment and clinical decision support. TIAM = Time Integrated Activity Map.
- FIGURE 4.
AI ecosystem is a complex environment in which AI system development occurs. The ecosystem connects stakeholders from industry to regulatory agencies, physicians, patients, health systems, and payers. Proposed SNMMI AI Center of Excellence can serve as an honest broker to empower the AI ecosystem from a neutral standpoint with focus on solutions. ACE = SNMMI AI Center of Excellence; RIS = radiology information system.
- FIGURE 5.
Twelve core concepts critical to trustworthy AI ecosystems.
Tables
- TABLE 1.
Opportunities and Challenges Ahead for Nuclear Medicine Toward Achieving Trustworthy AI
Category Domain Subdomain Opportunities Diagnostic imaging Emerging nuclear imaging approaches RPTs AI-driven theranostic drug discovery and labeling Precision dosimetry Predictive dosimetry and digital twins Clinical workflow: increasing throughput while maintaining excellence Challenges Development of AI applications/medical devices Data Optimal network architecture Measurement and communication of uncertainty Clinically impactful use cases Team science Evaluation (verification of performance) Performance profiling through task-based evaluations Guidelines for validation Multicenter clinical trial network Ethical, regulatory, and legal ambiguities Ethical aspects Regulatory and legal aspects Implementation of clinical AI solutions and postimplementation monitoring AI platform Barriers of dissemination and implementation of AI technology in medicine Postdeployment: change management and performance Trust and trustworthiness
Supplemental Data
Files in this Data Supplement:
In this issue
Jump to section
Related Articles
Cited By...
- Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging
- Green Nuclear Medicine and Radiotheranostics
- Is the Clinical Implementation of In-House Artificial Intelligence-Developed Algorithms Happening?
- Navigating the Future of Prostate Cancer Care: AI-Driven Imaging and Theranostics Through the Lens of RELAINCE
- Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance