Is There Value for Artificial Intelligence Applications in Molecular Imaging and Nuclear Medicine?

Abstract

Core competencies in molecular imaging and nuclear medicine include imaging with radioactive isotopes, pattern recognition, image interpretation, and report communication. In diagnostic imaging, nuclear medicine physicians communicate with referring physicians to establish indications for imaging studies, perform or supervise imaging procedures to obtain high-quality images, interpret images to compile medical reports, and communicate results to inform patients and their referring doctors. In many of these tasks, computers already are indispensable tools that enable or assist the work of physicians. How does artificial intelligence (AI) fit into this workflow (Fig. 1)? There is no universal definition of AI (1), but in the context of practical applications, AI can be considered a scientific discipline that uses computers to perform tasks usually requiring human cognition. Research topics on AI include pattern recognition, natural language processing, machine learning, problem solving, and knowledge representation. Thus, AI techniques apply to many core competencies in metabolic imaging and nuclear medicine, and this has led to an enormous interest and even hype about AI in medical imaging (2). AI applications at present dominate the technical exhibitions of international imaging conferences such as the 2019 European Congress of Radiology (https://ecronline.myesr.org/ecr2019/) and are prominently featured in the product portfolios of most commercial companies of medical imaging technology. AI applications have enabled a multibillion-dollar business model for the large Internet companies penetrating the consumer market in many ways, including Internet searches, social networks, or smartphone software. In consumer markets, value is measured by the willingness of a consumer to use or pay for a product. Therefore, the value of AI applications in consumer markets depends not on an intrinsic objective value but the perception of the customer. This value concept is well illustrated by a quote attributed to Charles Revson, the founder of a cosmetics company: ‘‘In the factory we make cosmetics, in the store we sell hope.’’ However, in medicine the business model and the value concept of a consumer market do not apply. Hope is essential when taking care of patients, but hope cannot serve as the foundation for medical decision making and patient stratification. Several decades ago, patients with extrasystoles after a myocardial infarction were commonly treated with antiarrhythmic drugs in the hope of preventing arrhythmic death. A large, randomized multicenter trial uncovered this hope as a deadly illusion and established that the antiarrhythmic therapy in fact increased arrhythmic mortality and caused premature death in thousands of patients. From this trial and others that were able to scientifically disprove suggested or assumed benefits based on hope and hype, rigorous and often laborious scientific methods have emerged that permit establishing, grading, or disproving the value of diagnostic and therapeutic procedures. For patients, value is generated if quality of life is improved, morbidity is reduced, or preventable mortality is eliminated. It is difficult or impossible for both patients and physicians to directly assess the value of medical products, interventions, or technology. Thus, science is essential to firmly guide patient care. International guidelines compiled by professional societies assess the value of diagnostic and therapeutic methods based on scientific evidence and, for each method, clearly state the class of recommendation for a particular use with an associated level of evidence. This science-based model of grading value for patient care has been universally adopted in the medical community. Currently, there is little scientific evidence for the value of AI applications in medical imaging. Several AI applications have received Food and Drug Administration approval (3), but this does not imply that AI applications at present are relevant for medical practice or that their value has been firmly established. Most AI applications are built from 3 essential components: complex computer algorithms, extensive computing resources, and large data sets. Algorithms and computing facilities are easily available at little or no cost. Many powerful AI algorithms are published as opensource code, such as TensorFlow (https://www.tensorflow.org) and Core ML (https://developer.apple.com/machine-learning/). Extensive computing power is offered instantaneously on demand through the large Internet companies. Recently, even smartphones have been equipped with high-power computing hardware, including neural network chips that enable intensive AI applications, such as facial identification or speech recognition. In contrast, large data sets are more difficult to collect and thus are the most critical component when building AI applications. Data are more important than hardware or software in determining the success of AI applications (4). Even highly complex AI algorithms cannot compensate for incomplete, inadequate, or low-quality data collection. Thus, the characteristics and validity of data sets need to be firmly established and made fully transparent when AI applications are investigated for a proposed clinical purpose. Collaborative efforts are frequently required to compile the large data sets, which need to include many thousands of data entries. Recently, the Mozilla Common Voice project (https://voice. mozilla.org/en) has published an open-source multilanguage data set of voice recordings from 40,000 people in 18 languages to foster and enable research in natural language processing. If AI applications are to be applied successfully in medical imaging, Received Mar. 18, 2019; revision accepted Apr. 3, 2019. For correspondence or reprints contact: Gerold Porenta, Ambulatorium Döbling, Heiligenstädterstrasse 62-64, 1190 Vienna, Austria. E-mail: [email protected] Published online May 3, 2019. COPYRIGHT© 2019 by the Society of Nuclear Medicine and Molecular Imaging. DOI: 10.2967/jnumed.119.227702