Section 1: Technical Background
Can machines think and act like humans? That’s the question Artificial Intelligence (AI) seeks to answer. AI has been defined in different ways. It’s been described as the branch of computer science focusing on creating machines that can solve problems that would otherwise need human intelligence to tackle. It’s been differentiated from Natural Intelligence (NI)—found in humans and animals—as lacking in consciousness and emotionality. At its core, AI attempts to simulate human and rational thinking and acting in machines.
Depending on the scope and complexity of tasks that it can handle, AI has been categorized as ‘weak’ or ‘strong’. Weak AI, also known as Narrow AI, specializes in single tasks. While it can do any one task extremely well, such AI is operating under extremely limited conditions and cannot cross over to functions it’s not programmed to perform. Examples of weak AI are search engines and image recognition software.
Strong AI—or Artificial General Intelligence (AGI)—is what someone would call true intelligence in machines on par with humans. This is the elusive goal that AI scientists are after. Machines that think, communicate and behave like humans. And like humans, such AI is not limited to a specific set of tasks but can apply its ‘intelligence’ to solve any number of problems and where it’s presented with a novel problem, it can learn and adapt like human intelligence to deal with it.
Understanding the Technology
Machine learning endeavors to create artificial systems that can learn and evolve autonomously from experience and exposure to data without being restricted by rigid sets of instructions or programs. In this regard, machine learning is an attempt to mimic intuitive human learning in computers. Computers have access to large volumes of data on which they train and learn automatically and improve their problem-solving ability over time. There are minimal external constraints in terms of protocols or instructions. The system is supposed to learn and improve by itself.
In supervised machine learning, the training data set presented to the system is known and labeled. The learning algorithms then enable the machine to analyze this data to make predictions about future outputs. On the other hand, unsupervised machine learning involves unlabeled data. The underlying algorithms here are more advanced in the sense that they are handling uncategorized data and learning how to explore hidden structures and patterns in it.
Algorithms are sets of mathematical instructions that instruct machines on what to do and how to do it. What an AI system is capable of achieving would depend on the type of algorithms it’s built on. Algorithms are akin to computer programs that tell a computer what to do step by step. Of note here, algorithms have evolved over time from a rigid set of parameters to models with inherent flexibility and fluidity that enables computers to learn and discern on their own via a process known as machine learning. Machine learning algorithms have been categorized as related to classification, regression and clustering.
Some Examples of Machine Learning Algorithms
To compute data category
Support vector machines
To forecast or predict
To group similar item clusters
Deep learning is an advanced form of machine learning and the new frontier in AI research. Its machine learning that utilizes the immense computational power of neural networks. Neural networks—that is, interconnected webs of neurons—are what drive the human brain. Deep learning is an attempt to simulate the brain’s workings in computer systems, using similar networks called artificial neural networks.
Based on such a sophisticated design, deep learning is capable of achieving what traditional machine learning can never aspire to do. Deep learning is hierarchical and layered—hence the term ‘deep’—so that it can perform computations at multiple levels at the same time. This is in contrast to traditional machine learning which is built to handle analyses in a linear approach. The end result is that its performance tends to plateau off over time. Deep learning, on the other hand, has immense scalability and just grows better as it is fed more data. In fact, deep learning makes the best use of two resources that have become the most important fuels of AI growth: big data and computational power. Big data is the term applied to the availability and access to enormous volumes of data that was never available to this magnitude in the past. Since AI systems learn and train on data, the more data available the better learning systems become. Similarly, computer power has grown exponentially over the years. Combined, such factors provide a fertile breeding ground for artificial neural networks to thrive and deep learning to prosper.
Another way deep learning is more sophisticated than traditional machine learning is that it is extremely capable of unsupervised learning on unlabeled datasets, so it requires minimal human input. Some areas where deep learning is making its mark are natural language processing (NLP), speech recognition and ecommerce.
Neural networks—also referred to as artificial neural networks (ANNs) or simulated neural networks (SNNs)—provide the functional infrastructure that permits deep learning in AI systems. In this sense, they are a prerequisite for deep learning in machines. Neural networks are reminiscent of the webs of neurons found in the human brain and the countless connections between them, called synapses. Replicating that structure allows artificial neural networks (ANNs) to simulate the workings of the human brain. Consequently, ANNs lead the way in most AI applications today.
An ANN can be thought of as an interconnected net of nodes—where a node is analogous to a neuron in the brain. The nodes are arranged in layers and the greater the number of layers, the more powerful the network becomes. This hierarchical organization gives rise to the term ‘deep’ learning.
Figure 1: A neural network in the brain (A) vs. an artificial neural network (B).
[The images are to serve as cues for your designer/illustrator. They have been taken from the web and may be subject to copyright.]
Neural networks are the powerhouses of AI systems. They enable the performance of complex tasks by computers such as speech and image recognition. They can be trained on big data to become increasingly efficient and accurate over time.
A popular type of neural networks is the feedforward neural networks, or multi-layer perceptrons (MLPs). The description given above is mainly about such networks. They can solve extremely complex problems such as natural language processing (NLP) and computer vision. They also provide the basis for other neural networks. Convolutional neural networks (CNNs) are somewhat similar to feedforward networks. They are highly capable of tasks such as image and pattern recognition. This makes them the preferred networks for AI applications in fields such as radiology. Recurrent neural networks (RNNs) comprise feedback loops that are particularly suited to predict future outcomes. Their applications include stock market forecasting.
A Glance at the Timeline
Alan Turing explores the possibility of thinking machines and artificial intelligence in his paper “Computing Machinery and Intelligence.” He proposes the now famous Turing test, which describes a machine as having achieved intelligence on par with humans if an observer cannot discern its responses from that of a real person.
John McCarthy and Marvin Minsky host the historic Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) conference. This was the event where McCarthy originally used the term ‘artificial intelligence.’ The conference spurred substantial interest and activity in the field. It is considered a seminal event on the AI timeline.
The AI pursuit sees cycles of achievements and setbacks. While mathematical models and algorithms are there, computing power has yet to catch up. Computers are just not powerful enough yet. This leads to a slowing down of AI research during these years and funding cuts across the board. The time period is called the “First AI Winter.”
Japan funds its ambitious Fifth Generation Computer Systems (FGCS) project with the aim to achieve supercomputer performance that could boost efforts at AI development. The UK and USA respond with enhanced funding for their own programs. The net effect is a stimulation of activity in AI research.
Different initiatives and programs across the globe fail to achieve desired goals leading to the “Second AI Winter.” Government funding in AI research drops in different countries. Supercomputers seem to be too costly to use at a widespread level. In the meantime, alternative affordable computing technologies continue to emerge.
IBM’s Deep Blue computer makes history when it defeats world chess champion Gary Kasparov.
Google makes available speech recognition for smartphones, which is considered a major step forward in bringing the power of AI into common use.
Google DeepMind’s AlphaGo beats Lee Sedol, a world champion, at the ancient Chinese game of Go, underscoring the immense progress AI research has made over the years.
Section 2: Practical Applications
Artificial Intelligence in Radiology
As we discussed above, the prowess of AI in image and pattern recognition, the amazing ability of deep learning architectures to adapt and evolve, and the unique proficiency of convoluted neural networks at image-based tasks translate into a huge scope for AI in radiology. Let’s explore further how AI solutions are impacting every aspect of the radiology workflow.
Applications in the Clinical Radiology Workflow
The clinical radiology workflow can be divided into a series of steps, as shown below (Fig. 2):
Figure 2: Clinical Radiology Workflow.
Let’s review the scope and application of AI, particularly deep learning architectures, in each of these steps:
The clinical radiology workflow begins with acquisition of the images. This is achieved through different types of hardware, for example, computed tomography (CT) and magnetic resonance imaging (MRI) scanners. Such hardware is driven by software for image reconstruction. Over time, scanning hardware has become increasingly efficient in terms of quality and resolution. Image reconstruction algorithms, on the other hand, still need to catch up. Deep learning research in this context has been aimed at providing the mechanisms to achieve image reconstruction transformations that match the sophistication of the hardware.
An important step in the preprocessing of acquired medical images is image registration. It is a process that aligns medical images spatially or temporally, bringing them into a single coordinate system. The result is that fusion images are created and quantitative analyses can be performed. Image registration algorithms so far have relied on predefined feature-based criteria. This limits both their scope and scalability.
Deep learning techniques have demonstrated that they can overcome these shortcomings since they are non-rigid, consistent and much faster. Furthermore, deep learning is multimodal so it can handle multimodal imaging such as hybrid PET scans for cancer. Deep learning nets that are based on recurrent neural networks (RNNs) are particularly suited to temporal image registration solutions.
Clinical image-based tasks include detection, characterization and monitoring:
Figure 3: Image-based tasks.
In routine radiology practice, the detection of anomalies on medical images is a manual and labor-intensive process that requires the presence of a trained and experienced professional—the radiologist. Radiologists have to go through a large volume of image slices as part of their daily workflow to detect anomalies on the basis of minute differences of texture, intensity or pattern from normal tissue. This is a time-consuming, subjective and error-prone process.
Computer-aided detection (CAD) has been an area of active research and development for quite some time now. However, CAD systems developed so far have exhibited subhuman performance and have not been able to transition to routine radiology practice in a significant way. Once again, with the latest boost in deep learning science, there has been a renewed interest in the possibility of CAD systems that match or outperform experienced radiologists. The results have been promising. In a recent study that involved the detection of lesions on mammograms, CAD built on deep learning architecture performed better than traditional machine learning CAD systems and comparably to radiologists.
Once an anomaly has been detected on a medical image, it has to be characterized. Characterization of the disease involves clinical steps such as segmentation, diagnosis and staging. Each of these steps requires the meticulous attention of a radiologist who has to painstakingly go through each image to discern subtle changes of texture and structure.
- Segmentation refers to delineating and demarcating anomalies on medical images. This is required for disease monitoring and treatment planning, such as for radiation therapy. Automated segmentation solutions have been around for several decades. Segmentation algorithms utilize the principles of clustered imaging intensities, region growing around seed points and probabilistic atlases.
- Diagnosis includes determining whether the lesion is benign or malignant. Computer-aided diagnosis systems have traditionally been fed with predefined tumor radiographic criteria such as related to size, sphericity, margin and texture. They have mostly served to assist with the work of radiologists and are still not advanced enough to act as standalone solutions. Since deep learning is not dependent on predefined input and additionally it is more noise tolerant, computer-aided diagnosis solutions built around it promise to be more effective and generalizable.
- Staging involves ascertaining the extent of the disease. A classic example is the TNM (tumor, node and metastasis) staging system that categorizes patients on the basis of tumor characteristics, involvement of regional lymph nodes and spread (metastasis) to other body areas. Such complex staging systems rely on expert opinion and lie beyond the realm of traditional machine learning. However, deep learning, with its ability to integrate several layers of information simultaneously, has the inherent ability to power accurate automated staging solutions in the near future.
Monitoring disease is necessary to gauge treatment response and pick up recurrence. Imaging software algorithms assist with monitoring tasks by corresponding relevant images across multiple scans over time and highlighting subtle changes in lesion parameters such texture, size, heterogeneity or cavitation. The process depends on the efficiency of the system at image preprocessing and registration as well as the predefined features. Deep learning methods based on recurrent neural networks (RNNs) are particularly efficient at temporal monitoring tasks and are being actively researched for this purpose.
Reporting is an essential component of the radiology workflow as it is the means to communicate all pertinent clinical findings in the patient. What other physicians and clinical departments would determine about the patient would depend on the contents of the report. There is still no single universally accepted format for radiology patient reports. Most are text-based and created from the consultant’s dictation. Such variability in reporting gives rise to gaps in clinical communication that hamper effective patient management. Furthermore, creating reports is often a manual and time-consuming process. Deep learning algorithms are adept at multitasking. They make possible computer vision, image discrimination and voice recognition. By leveraging their diverse power, radiology reporting can become an automated process that is faster as well as more accurate, consistent and interactive.
Finally, AI can assist in the integration and evaluation of patient data from multiple sources. Such sources can include radiology reports, laboratory test results, clinical examination and even data from remote monitoring devices such as fitness trackers. This would inevitably lead to the generation of new insights into patients’ health and improve clinical outcomes.
Clinical Use Cases for Various Specialties
The principles of medical image segmentation that we discussed above, particularly in light of the role of AI algorithms, have found practical applications in cardiac imaging as well. A recent method, multi-scale deep reinforcement learning, has been able to successfully delineate cardiac anatomical landmarks. When combined with labeled learning protocols, such systems are able to accurately segment heart structures such as cardiac chambers, valves and the coronary arteries. Furthermore, they can assist with the measurement of functional parameters such as the ejection fraction. AI-based heart imaging solutions have demonstrated utility in interventional cardiology and for automated measurements in echocardiography. Hierarchical clustering AI techniques have been employed in the study of heterogeneous heart diseases including hypertension and heart failure.
AI is having a clear impact on neuroimaging. Aside from the general ways in which AI improves image acquisition, reconstruction and registration as well as anomaly detection and characterization, deep learning-based architectures have been leveraged to create novel neuroimaging tools that serve distinct purposes. For example:
- Enhancing image quality and reducing noise and scan acquisition time significantly for MRI and PET-CT neuroimaging.
- Detecting acute lesions such as intracranial hemorrhages and cervical spine fractures on head and spine CTs.
- Aiding in the diagnosis and monitoring of brain diseases such as multiple sclerosis and Alzheimer’s disease.
- Improving the yield of brain MRIs by assisting with quantitative volumetric analyses.
Screening mammography is an important tool for the early detection and diagnosis of breast lesions. Yet, it can be challenging to manually discern all the minute changes of texture, structure and density in images of breast tissue. AI has been able to assist in identifying breast lesions, such as microcalcifications. While traditional AI solutions for mammography have only meant to support radiologists at basic image interpretation, deep learning AI offers more possibilities in terms of comprehensive mammography software suites. For example, a recent study showed that architectures based on convolutional neural networks (CNNs) performed better than traditional diagnostic systems and comparably to radiologists in the detection of lesions on mammograms.
Detecting and characterizing pulmonary nodules on lung CTs can be a tedious and meticulous affair. AI can automate the process and aid in determining whether the nodules are benign or malignant. AI-based radiographic image biomarkers can help in the detection and surveillance of lung cancer. Furthermore, AI can assist in differentiating solid lung nodules from non-solid ground-glass opacity (GGO) nodules, which can be a diagnostic challenge.
Treatment planning in radiation oncology rests on the accuracy of segmentation of tumor and normal tissue. We discussed segmentation as an important step in the clinical radiology workflow. Traditional segmentation algorithms have relied on clustered imaging intensities or region growing. Examples of advanced segmentation systems are probabilistic atlases. Deep learning techniques have vastly improved the efficiency and accuracy of segmentation of medical images. This has been of great help to radiation oncologists. In addition, AI can aid with the tracking of treatment response after radiation therapy cycles.
Abdominal and Pelvic Imaging
Deep learning has demonstrated a promising role in abdominal organ segmentation, including organs such as the liver, pancreas, stomach, kidneys, spleen and prostate. Convolutional neural networks (CNNs) have been shown to enhance the segmentation of abdominal organs in T2-weighted MR images. AI can be used to explore incidental findings on abdominal scans such liver lesions. It can also be used to characterize and track colonic polyps.
How does AI help Radiology and Radiologists?
We just discussed many practical applications of AI in radiology. Let’s go over the key ways in which AI helps radiology and radiologists:
Improved Workflow Efficiency
AI can be likened to any other tool. It enhances our performance and productivity. There is only so much that radiologists can do manually on a given work day. On the other hand, imaging data increases exponentially each year. In the US alone, millions of medical images are generated each year. It takes many years to train a radiologist—as opposed to deep learning architectures that can be trained much faster. There is an evident mismatch between workload and available human expertise. We have just seen how AI is able to handle tasks at every step of the clinical radiology workflow. With the continued integration of AI in radiology, there is an ongoing improvement in clinical workflow efficiency for radiologists. Each task that AI takes over is one action less for the radiologist to perform manually.
Enhanced Workflow Quality
There are limitations to human perception. Some changes in pixel intensities are too subtle for radiologists to pick up, yet they can still be detected by machines. Similarly, radiologists need processed and prepared images to work on, but machines can utilize the raw data from image acquisition—which contains more information. Deep learning systems, once trained and optimized, can integrate cues from multiple input streams to arrive at inferences through processes that work faster and at a larger scale than is possible for humans. All these factors indicate that as radiology AI systems continue to mature, they will improve image workflow quality in ways hitherto considered unattainable.
Radiomics and Imaging Biomarkers
Radiomics is a great example of what AI can achieve in medical image analysis. Radiomics leverages deep learning algorithms to characterize and quantify medical imaging data and give it clinical context. The ever-increasing volume of medical imaging data has provided a fertile ground for the training of radiomics algorithms, increasing their efficiency over time. Radiomics has made possible the generation of novel imaging biomarkers. Utilizing such biomarkers, radiomics AI can assist with disease prognostication and forecasting clinical outcomes, data visualization, prediction of metastasis risk, assessment of treatment response and in the planning and administration of radiation therapy.
Clinical Decision Support
AI systems provide clinical decision support to radiologists. This begins right from the triage of patients where such systems can guide on both the acute need of an imaging procedure and the most suitable scan for a particular patient. Once imaging data has been acquired, AI-based clinical decision support systems can aid along each step of patient management by assisting the radiologist with clinical adjudications such as disease risk stratification and prognostication as well as treatment planning and response monitoring. In addition, deep learning algorithms can detect subtle findings on medical imaging that may potentially be missed and then present those to radiologists who can then further decide on their clinical significance.
AI solutions achieve cost savings for facilities by improving both workflow efficiency and quality. Since the radiology department is so interconnected with other departments in a hospital, the economic benefit translates to savings for the institution as a whole and not just for radiology. Let’s look at some of the ways how implementing AI in radiology leads to cost savings:
- Faster scan reading times means that radiologists can get more work done in less time. This directly translates to cost savings in terms of radiologists’ hours utilized and compensated. AI algorithms can enable this by narrowing down images with pertinent findings that radiologists can look into further, instead of going through every single image. In addition, they can reduce false positives per image. In one study, the investigators estimated that an AI-based CAD software reduced reading time per case by 17%.
- Better workflow organization is achieved when AI systems assist in prioritizing images for radiologists to review. The end result is enhanced workflow efficiency as radiologists do not need to go through all images to catch a few that need their immediate attention. Time is saved and thereby costs as well.
- Improved risk assessment leads to more targeted patient management, with patients deemed at greater risk of disease spread receiving the most resources. This promotes overall health outcomes and conserves precious healthcare resources by facilitating their judicious use.
- Reduced downstream costs is perhaps the most important cost saving aspect of AI in radiology, yet one that is often underestimated. Deep learning CAD systems can improve the sensitivity of screening scans and detect subtle anomalies that could otherwise have been missed. For almost any disease but especially for cancers, early detection means more chances of a complete cure with fewer resources spent. When these cases are missed and present later with advanced or complicated disease, the economic burden they represent is in orders of magnitude higher. Since such cost savings are hard to measure prospectively, they are often underestimated.
The Fear: Could AI replace Radiologists?
AI has got a long way to go before it catches up with radiologists, let alone replaces them. Most current applications in the field can be categorized as narrow AI that is good at single tasks but lacks in comprehensive application. A general AI on par with human intelligence is still an elusive goal. In this context, AI in radiology is still in its infancy.
Instead of fearing AI, radiologists should consider it as a tool that is meant to enhance their performance. AI can take care of mundane repetitive tasks in radiology. It can assist radiologists in increasing their productivity as well as accuracy. It can help them tackle imaging big data that grows by leaps and bounds every year. AI, it seems, is a friend and not a foe.
While AI is getting better by the day at image-focused tasks, it’s far away from giving findings meaningful clinical context. This indicates an expanding albeit different role for radiologists in the future where AI could perform most of the imaging data evaluations and radiologists could give the results clinical meaning and decide how they factor into patient management.
In addition, the role of radiologists can expand in novel directions such as training AI and overseeing its development. Most AI algorithms are developed by engineers and software scientists. Radiologists can bring a much needed clinical perspective to the table. Once again, instead of avoiding AI, radiologists can actually take the driving seat in its development and make sure that it grows in a direction that benefits patients the most.
Section 3: Industry Overview and Technical Implementation
AI in Radiology—Industry Overview
According to estimates, the global market for AI in medical imaging stood at USD 21.5 million in 2018 and is projected to reach USD 181.1 million by 2025 and 264.85 million by 2026, representing a compound annual growth rate (CAGR) of 35.9%.
The market can be categorized as moderately fragmented. While established players are driving consolidation through acquisitions and mergers, the sheer pace of innovation means that new startups are continually popping up while older systems and solutions fall out of favor. Some recognized names in the industry are GE Healthcare, Siemens AG, Philips Healthcare, Samsung Electronics, IBM Watson Health, Medtronic, Enlitic Inc, Nvidia Corporation, IBM Corporation, Agfa Healthcare, Intel Corporation, Johnson & Johnson and Microsoft Corporation.
Drivers of industry growth include the ever-increasing use of medical imaging in healthcare generating millions of images each year that exponentially increases workload for radiologists, shortage of trained and experienced radiologists—particularly in some geographic regions, improvement in workflow efficiency and quality associated with reliable AI solutions, and cost savings that come with autonomous systems. On the other hand, barriers to industry growth include regulatory and ethical issues, a lack of complete mathematical understanding of how deep learning architectures work, slow uptake by healthcare facilities such as hospitals and clinics, and a dearth of practical comprehensive AI solutions as most current systems solve single tasks. Regarding the slow uptake by healthcare facilities, there is a need to educate and inform health care professionals (HCPs), radiologists and hospital administrators about the availability and benefits of radiology AI solutions on the market. Increased clinical uptake and utilization can spur the growth and refinement of radiology AI systems.
Among geographic regions, while North America, particularly the US, is set to remain the leader in AI research and development (R&D), future growth is predicted to occur fastest in the Asia-Pacific region. Key countries in this regard are China and India with their vast populations, increasing disposable incomes and growing focus on healthcare. For example, in China, medical imaging volume expands by 30% each year while the number of radiologists increases by 4% only. No wonder, China is at the forefront of AI research in medical applications with many success stories.
A mention here of the impact of COVID on the industry is necessary. While COVID has been responsible for a dampening effect on the development of medical imaging AI solutions as well as their clinical validation and uptake by healthcare institutions, it has also triggered a flurry of activity in finding AI applications that aid with chest imaging analyses—for the screening, diagnosis and monitoring of COVID-related lung lesions. COVID primarily targets the lungs and chest X-rays and CTs are indispensable in its clinical management—for tasks such as diagnosis, determining disease severity and monitoring treatment response. Companies and organizations across the globe are putting in the extra effort to quickly come up with effective AI solutions for COVID-related medical imaging evaluation. In this context, COVID has proven a catalyst for the growth of AI in radiology.
AI in Radiology—Technical Implementation
Cloud vs. On-Premises
When radiology departments decide to try out AI software, they are often faced with the choice of going with a cloud vs. on-premises solution. Cloud-based AI software is kept and maintained on the vendor’s own servers and is accessed by the client via the internet through an interface such as a browser or dedicated dashboard. The vendor licenses the application to the client as software as a service (SaaS) paid on a subscription model that involves recurring payments, often monthly or annual. Such a license is referred to as a subscription software license. Conversely, on-premises solutions are installed locally by the client on their computer systems and usually paid for once and upfront. Costs involved include the licensing agreement and service charges. The license in this case is called a perpetual software license.
Big hospitals with established IT departments may prefer on-premises radiology AI software that gives them more autonomy and they can take care of basic troubleshooting as well as maintenance of the hardware. However, for most clients, it seems that cloud-based services offer the most benefits. With cloud radiology AI software, the vendor maintains the hardware such as servers and storage, keeps the software updated and glitch-free, and ensures optimal uptime. When clients do not have to take care of all these tasks at their end, it translates to substantial time and cost savings. Furthermore, without the distraction of IT maintenance, clients can focus on their core mission—helping patients. It is important to mention here that modern AI vendors are HIPAA-compliant and understand the importance of maintaining the privacy of patient data. In fact, today’s cloud-based AI solutions for healthcare are as sturdy and secure as local installations, if not more. Of course, this assumes that the vendor is a reliable and reputable company.
Purchasing Decisions and Costs
When radiology practices are contemplating the purchase of medical imaging AI software, the costs involved are a crucial consideration. Any expenditure in this context has to be justified from two angles. First and foremost, does the purchase make a business case, that is, would it have such a positive impact on the workflow to pay for itself? Secondly, would a cloud-based service or on-premises installation make more sense, in terms of both costs and clinical requirements?
With reference to building a business case, medical imaging AI solutions all aim at making the radiology clinical workflow more efficient and accurate, thereby cutting costs. For example, AI software used in screening mammography clinics to assist with the detection and characterization of breast lesions substantially reduces costs both immediately and in the long term. Immediately by speeding up radiologist reading time per case and reducing false positives per image. In the long term by decreasing missed cases with future clinical and financial implications.
As regards cloud vs. on-premises software, it is easy to assume that a one-time payment for an on-premises installation, even if bigger initially, would be cheaper in the long run compared with the recurring payments of a cloud subscription. What is often overlooked is that an on-premises installation would require regular software and hardware maintenance, the presence of dedicated IT staff and the expenses associated with upgrades. All these represent substantial if not clear-cut costs. A cloud-based solution, as we described above, does not come with such ‘hidden’ costs. Cloud-based medical imaging AI solutions are scalable and secure, and maintained and updated by the vendor. More often than not, they mean more bang for your buck.
Integration and Interoperability
Integrated solutions improve workflow efficiency, reduce redundancy and ensure an overall smooth user experience. For a new radiology AI software, a big challenge for the developers is its seamless integration into the existing workflow. Standalone AI systems fragment the workflow. They need independent workstations and then someone to perform the extra steps of feeding them the data that needs to be analyzed and procuring and transferring the results to the main information architecture of the healthcare facility. Furthermore, such systems face more uptake hesitancy from radiologists as they see them as rife with additional tasks to add to their already brimming workloads. Therefore, radiology AI developers prioritize building software that integrates easily with the rest of the information architecture of hospitals and clinics.
So, what is this information architecture of healthcare facilities that we just mentioned? Hospitals and clinics use a variety of IT applications to expedite their workflow, hasten their turnaround times and bolster their productivity. At the hospital level, two indispensable and mandated tools are the EHR and EMR. The electronic health record (EHR) system is the backbone of any healthcare facility’s digital communication strategy. It is a facility-wide network that collects, stores and shares demographic and clinical data of patients in a digital format. The electronic medical record (EMR) system is linked to the EHR. It mainly deals with creating, maintaining and communicating electronic patient charts.
At the radiology department/imaging clinic level, key IT tools are the RIS, PACS and DICOM. The radiology information system (RIS) assists with order entry, patient scheduling and report generation. Where the RIS assists with patient management, the picture archiving and communication system (PACS) helps with medical imaging data management. The PACS server organizes medical images and stores them securely. It facilitates their retrieval and distribution. When medical images are shared and viewed, they are done so in a standardized format called the Digital Imaging and Communications in Medicine (DICOM) standard. While these are essential IT systems for any radiology practice, there are many task-based software programs available as well. A notable mention here would be of PowerScribe 360 Reporting, a real-time radiology reporting platform. Its cloud-based version is called PowerScribe One. PowerScribe leverages AI-powered speech recognition technology to assist radiologists with radiology reporting and workflow management. Being used by almost 80% of radiologists in the US, PowerScribe has become another core tool for radiology practices.
Developers and vendors of radiology AI solutions seek the integration and interoperability of their products through different approaches. Where a medical imaging center is specialized in a particular type of imaging, for example, screening mammography, a standalone station offering an AI application for a defined set of analyses can be a practical option. In most cases however, better integration is necessary. One approach is to link the AI software with one of the key IT systems, for instance, the RIS or PACS. This would depend on the nature of the AI software: one dealing with improving patient flow would need to be connected to the RIS, while one that facilitates medical imaging data analyses would require linking up with the PACS server. With such an integration, the AI algorithm can autonomously collect the data it needs from the IT system it is linked to, run its analyses and send back the results. Users can access the results through the broader IT system that they are already trained on and used to working with. They do not have to take any extra steps or go through another learning curve to be able to use the new AI software. Finally, platform companies are specialized in AI solutions integration. When a radiology practice intends to utilize many different AI solutions, a good way forward is that they collaborate with a platform company to handle the integration and interoperability of these varied AI applications.
Section 4: State of AI in Radiology
Radiology AI—Current Challenges
A Comprehensive AI
The ultimate goal of scientists and researchers is to create an AI that, like human intelligence, can autonomously learn any task it sets upon via inbuilt processes such as trial and error and reiterations—in other words, a ‘strong AI’ or Artificial General Intelligence (AGI). Such an AI would be the closest thing to a radiologist because not only would it excel at image-related analyses, it would also ‘understand’ what those findings mean in a clinical context and correlate patient data from multiple sources to ‘decide’ the appropriate next management steps for a particular patient.
At the moment, most AI applications in radiology are task-based focusing on single tasks at a time—that is, they fall under the category of narrow AI. While such systems may excel at well-defined image-based tasks, they are incapable of taking into account the bigger clinical situation. Devising a comprehensive, general AI is a true challenge and when—not if—achieved, it will dramatically change the radiology landscape as we currently know it.
Data Volume and Curation
As we discussed before, deep learning loves big data. The more data to train on, the better deep learning systems get over time. Big data in radiology is now a possibility with millions of medical images generated every year. However, while deep learning systems are being developed at software houses, medical images get accumulated at healthcare facilities. One challenge has been to devise different methods of imaging data sharing to ensure the continual availability of training datasets. Technologies such as the Picture Archiving and Communication System (PACS) facilitate the sharing of medical imaging data, while legislation such as the Health Insurance Portability and Accountability Act (HIPAA) ensures that patient privacy is maintained. Furthermore, the emergence of large repositories of medical imaging data has been a welcome development in this regard as they serve as ready sources of imaging data for algorithm training.
And just the availability of large volumes of imaging data is not enough. It has to be curated. Curation refers to organizing, classifying and annotating data. In radiology, examples of data curation include segmentation of medical images and grouping and annotating images according to the patient cohort they represent. Machine learning requires curated data to train on. For instance, medical images curated to match a specific patient cohort can assist algorithms in learning how to correlate imaging and clinical endpoints. Yet, data curation is a manual process that is extremely time-consuming, and hence costly. Data curation has therefore been one of the barriers to the rapid development and deployment of AI applications in radiology. The solution it seems lies with AI itself, in the form of deep learning that is not dependent on labeled data. Deep learning architectures have unique data mining capabilities where they can learn to discern patterns in raw data on their own. This can eliminate the need to curate data, a laborious and lengthy process. In the meantime, public repositories of medical images and imaging biobanks have a crucial role to play in the advancement of machine learning because the medical imaging data they store is not only available in huge volumes but most of it is curated as well.
Unraveling AI’s Inner Workings
A unique challenge is that scientists still do not fully understand the inner workings of deep learning neural networks. While the input and output of such networks is easier to determine in terms of the training data and inferential endpoints, respectively, what goes on between those two layers—in the ‘hidden’ layers—is not fully known. Neural networks comprise thousands of highly interconnected nodes and deciphering all the activity that goes on between them is a challenge in its own right. While this may not be such a pressing problem in some other areas of AI application, when it comes to healthcare, understanding ‘what is going on’ becomes imperative as people’s health and lives are at stake. As researchers get better at unraveling the mathematical logic behind AI’s inner workings, it will greatly improve the application of AI in radiology. Not only will it enhance our knowledge of how radiology AI systems solve tasks, it would also improve troubleshooting in such systems as well as their refinement and evolution. AI applications in radiology then wouldn’t be referred to as ‘black-box medicine’ anymore.
The regulation of AI has been a hotly debated topic for a long time. Proponents believe the power and possibility that AI represents means that its growth needs to be kept under a close watch. Opponents believe tight regulation is going to stifle AI research efforts. In radiology, AI regulation looks at problems such as the opaque workings of AI systems, which we discussed above, and patient data privacy. Regulatory bodies include the Food and Drug Administration (FDA) in the US and the EU with its Medical Device Regulation. Legislation such as the Health Insurance Portability and Accountability Act (HIPAA) calls for the security and privacy of patient data. While ensuring patient data privacy is imperative, it impedes AI training which relies on the easy and ready availability of data. AI developers look at workarounds such as using encrypted data, using data on-site at hospitals and using publicly available anonymized data from medical image repositories and biobanks.
A key ethical question concerning AI is that when it makes a decision, who is responsible for the consequences? While the goal of AI is to create systems capable of thinking and acting like humans, a crucial consideration here is that humans are responsible for their actions. When an AI system decides and acts autonomously and errs, who is to blame? This is an area of active discussion. When it comes to radiology, this ambiguity can be a source of medicolegal issues. Currently, CAD systems only support radiologists with their workflow and do not independently adjudicate clinical decisions. Further along the line when AI systems become better than radiologists at more and more image-based tasks, they will start making important clinical calls—and then ethical issues surrounding the ownership of actions will take center stage.
Radiology AI—Future Outlook
If we put the ethical debate aside for a moment, the future of AI looks extremely promising. Big data continues to grow exponentially each year and so does computing power. AI research continues at a frenetic pace. Notably, it is not restricted to a few sectors. Whether it is self-driving cars, voice and image recognition, space exploration, finance and ecommerce, gaming and virtual reality, or healthcare and medicine, AI touches almost every aspect of modern human life. The thing with such a broadly applied scientific discipline is that if there is a breakthrough in one area, it translates to gains in all sectors. For instance, if AI research for self-driving cars, or ecommerce for that matter, yields new insights, those can be applied to healthcare as well—since it’s the same underlying deep learning algorithms that are powering AI all around. The point here is that AI seems bound to make major breakthroughs in the near future.
For radiologists, this should come as good news and not something to fear. AI can take over routine and laborious image-based tasks while radiologists can focus on how to leverage its power to improve patients’ health outcomes. The role of the radiologist may change in scope but is unlikely to be replaced. In fact, radiologists can help shape the future of AI in radiology by participating actively in its development.
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