Author + information
- Received August 8, 2018
- Revision received February 26, 2019
- Accepted April 2, 2019
- Published online July 15, 2019.
- Partha Sardar, MDa,
- J. Dawn Abbott, MDa,
- Amartya Kundu, MDb,
- Herbert D. Aronow, MDa,
- Juan F. Granada, MDc and
- Jay Giri, MD, MPHd,e,∗ (, )@parthasardarmd@jaygirimd
- aCardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island
- bDivision of Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts
- cCardiovascular Research Foundation, Columbia University Medical Center, New York, New York
- dPenn Cardiovascular Outcomes, Quality and Evaluative Research Center, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania
- eCardiovascular Medicine Division, University of Pennsylvania, Philadelphia, Pennsylvania
- ↵∗Address for correspondence:
Dr. Jay Giri, Hospital of the University of Pennsylvania, Cardiovascular Medicine Division, Gates Pavilion, 9th Floor, 3400 Spruce Street, Philadelphia, Pennsylvania 19104.
• The field of AI has initiated a paradigm shift in health care with advanced analytical techniques.
• Potential applications of AI in IC are image and video analysis, clinical decision support, robotic assistance with procedure, and novel approaches to clinical database analysis.
• The current development of AI in IC is in its early stage, but it has the potential to transform IC practice.
Access to big data analyzed by supercomputers using advanced mathematical algorithms (i.e., deep machine learning) has allowed for enhancement of cognitive output (i.e., visual imaging interpretation) to previously unseen levels and promises to fundamentally change the practice of medicine. This field, known as “artificial intelligence” (AI), is making significant progress in areas such as automated clinical decision making, medical imaging analysis, and interventional procedures, and has the potential to dramatically influence the practice of interventional cardiology. The unique nature of interventional cardiology makes it an ideal target for the development of AI-based technologies designed to improve real-time clinical decision making, streamline workflow in the catheterization laboratory, and standardize catheter-based procedures through advanced robotics. This review provides an introduction to AI by highlighting its scope, potential applications, and limitations in interventional cardiology.
Artificial Intelligence (AI) is a general term that signifies the use of mathematical algorithms which give machines the ability to reason and perform cognitive functions such as problem solving, object/word recognition and decision-making (1,2). AI encompasses a family of operations including machine learning (ML), deep learning (DL), natural language processing (NLP), cognitive computing, computer vision and robotics (Figure 1) that can be used to integrate and interpret complex biomedical data as well as advance technological automation (3–6). Years of research have finally brought AI into our daily lives with popular practical applications such as self-driving cars and speech recognition software such as Amazon’s Alexa Voice (Amazon, Seattle, Washington) and IBM’s Watson (IBM, Armonk, New York). However, the rate of adoption of AI in medicine is slow, as compared with other industries such as financial technology, information technology, and the aerospace industry.
The application of AI in interventional cardiology (IC) can be divided into 2 main branches, virtual and physical. The virtual branch includes informatics from ML/DL, NLP, and cognitive computing to control health management systems (i.e., electronic health records and medical image analysis software), and automated clinical decision support systems. The physical branch is best represented by robotic interventional procedures. Applications of AI within medicine have been predicted to fundamentally transform the landscape of health care delivery. However, as with many emerging technologies, the true promise of AI within medicine at large, and IC specifically, may be lost if it is not developed correctly.
Concepts Underlying AI Methods
AI is an umbrella term for a set of algorithms that imitate intelligent human behavior. ML refers to an automated system that learns to perform a task or make decisions automatically from an available data source, rather than having its behavior explicitly programmed (Table 1). Although the terms artificial intelligence and machine learning are often used more or less synonymously, in more precise terms, ML is a set of techniques to enable AI. AI and ML are used in several overlapping fields such as NLP, cognitive computing, computer vision, and robotics (Central Illustration). Major techniques/approaches in AI and ML include supervised, unsupervised, and reinforcement learning (Figure 1).
ML methods can complement and extend conventional statistical methods. Existing statistical methods primarily help to explore relationships between a limited number of variables. By contrast, ML methods identify features from the data, perform prediction, and provide tools and algorithms to understand patterns from large, complex, and heterogeneous data (7,8). Additionally, ML algorithms are based on fewer assumptions and can, in some cases, provide superior and more robust predictions (2). For example, Azzalini et al. (9) used generalized boosted regression (an ML approach) to identify whether contrast media type was an independent predictor of contrast-induced acute kidney injury after percutaneous coronary intervention (PCI).
DL is a subfield of ML and is characterized by algorithms that are inspired by the workings of the human brain, including a class of algorithms called neural networks (1,7) (Table 1, Figure 1). In the case of traditional ML, an algorithm needs to be programmed how to make a precise prediction by providing it with additional information. With DL, algorithms analyze large datasets, automatically discover patterns, and learn without human intervention. A DL neural network consists of digitized inputs, such as an image, audio or video, which proceeds through multiple layers of connected “neurons” that progressively identify features, and ultimately provides an output (1,2). DL is the underlying platform for image recognition applications that are likely to be used in cardiovascular imaging modalities (e.g., angiography, echocardiography, computed tomography, cardiac magnetic resonance, intravascular ultrasound, and optical coherence tomography) (7,8). Creating DL networks can take a significant amount of time and resources. The transfer learning approach, which involves transferring the knowledge gained by a DL system in 1 dataset to another dataset with different data, may mitigate this issue. (4).
Supervised learning is a type of ML in which training of datasets are conducted with specific labels or annotations (Figure 1). This approach develops a model to predict or classify future events, or to find variables relevant to a specified outcome. It often involves classification of an observation into one or more categories or outcomes (e.g., “Does this angiogram represent coronary dissection or thrombosis?”). Supervised learning involves classification and regression problems, but it requires a lot of data and is time-consuming, because the source data have to be labeled by humans (2,6,7). However, some successes have already been seen with supervised learning with models developed to successfully improve heart failure readmission prediction and accurately grade echocardiographic mitral regurgitation (8).
Unsupervised learning seeks to discover underlying structure or relationships among variables in a dataset (Figure 1). The training of dataset is conducted without any specific labels, and the algorithm clusters data to reveal underlying patterns. It seeks to identify novel disease mechanisms, genotypes, or phenotypes from hidden patterns present in the data (2,7). Unsupervised learning algorithms including artificial neural networks that analyzed surface echocardiograms have already been found to be useful in the automated discrimination of hypertrophic cardiomyopathy from physiological hypertrophy seen in athletes (10). Shah et al. (11) used agglomerative hierarchical clustering, a commonly used unsupervised learning tool for analysis of phenotypic data, and the phenomapping resulted in a novel classification of heart failure with preserved ejection fraction.
Reinforcement learning, based on behavioral psychology, uses an alternate approach where a software program acts in a pre-specified environment to maximize a reward (1,2) (Figure 1). The program thus identifies the appropriate behavior using a “reward criteria” to influence the decision-making process. It attempts to accomplish a task (e.g., driving a car, inferring medical decisions) while learning from its own success and failures (2,6). The main aim of reinforcement learning is to maximize the accuracy of algorithms using trial and error. Many clinical problems can be formatted to fit the format of a reinforcement learning problem. Hence, reinforcement learning algorithms may be used to aid clinical decision making, intelligently segment medical imaging data, and select personalized medications (1,2). Application of reinforcement learning to medicine and cardiology thus far has been limited. Studies have demonstrated promising results with reinforcement learning in optimization of treatment decisions for chronic illnesses and recommending mechanical ventilator weaning protocols that have led to superior clinical outcomes (7).
Natural language processing
NLP, a confluence of AI and linguistics, focused upon developing a computer’s ability to understand human language (6) (Central Illustration, Table 1). ML and DL have some overlap with NLP; however, NLP has a strong linguistics component (not represented in images) that requires an understanding of how we use language. NLP has been used for largescale database analysis of the electronic medical record (EMR) to detect adverse events and post-operative complications from physician documentation, to achieve automated claims coding, and to improve workflow (6,12). Sohn et al. (13) demonstrated the feasibility of NLP techniques in processing radiology reports and developed a rule-based algorithm to identify abdominal aortic aneurysm. NLP can be used to scan thousands of data sources (EMRs, image libraries, and so on) to screen and identify patients with critical valvular or vascular stenosis, hypertrophic cardiomyopathy, atrial septal defects, and so on.
Computer vision is a branch of computer science dealing with objects and feature recognition in digital images, including digital video frames (1,5). It is a combination of image processing (for feature extraction) plus ML (using these features to build a model). Unsupervised learning algorithms including different neural network can be used for model building. An important health care–related application of computer vision is acquisition and interpretation of cardiac images, including computer-aided diagnosis and image-guided procedures/surgery. A real-time analysis of laparoscopic videos yielded 92.8% accuracy in automated identification of the steps of sleeve gastrectomy and also successfully identified missing or unexpected steps (5). Although predictive video analysis is in its infancy, evidence exists that AI can be leveraged to process large amounts of interventional data to identify or predict real-time adverse events and assist intraprocedural clinical decision making.
Virtual Application of AI in IC
AI promises to have a major impact on imaging reconstruction, analysis and interpretation. The analysis of large amounts of imaging data and the use of dedicated imaging software has allowed the advancement of the imaging display into more “anatomic layouts” allowing the simplification of imaging interpretation (4,8). A study by Du et al. (14) showed the preliminary feasibility of DL technology in angiographic recognition based on a convolutional neural network model. For training data, 3,990 images were used to establish the model, and 2,711 images were used to evaluate it. The precision (percentage of the correct pixels) and recall rate (percentage of retrieved relevant pixels) were used to evaluate the effectiveness of the model. For detecting lesion characteristics, including diameter stenosis, calcification, thrombus, and dissection, the recall rate was 88.2%, 82.6%, 85.5%, and 85.8%, respectively (14). Another study by Ciusdel et al. (15), evaluated an AI-based solution for fully automated cardiac phase and end-diastolic frame detection on coronary angiograms. The workflow was trained with a deep neural network on 17,800 angiograms from 3,900 patients and evaluated on 27,900 angiograms from 6,250 patients. Cardiac phase detection had an accuracy of 92.6%, a sensitivity of 92.4% and a specificity of 92.9% on the evaluation set (15). A group from Emory University has developed an ML method for segmenting intravascular ultrasound images and automatically calculating lumen area and plaque burden that showed excellent agreement with an expert analyst (16). This method can segment single images in a fraction of a second and has the potential for online use in the catheterization laboratory.
The anatomic and functional assessment of coronary stenosis is now possible with the use of noninvasive imaging using DL (HeartFlow, Redwood City, California) (17). In the catheterization lab, computational fluid dynamics has allowed the development of wire-free algorithms for the detection of functionally significant coronary stenoses with use of computer vision and 3-dimensional reconstruction (angiography-based fractional flow reserve; Cathworks Ltd, Kfar-Saba, Israel) (18). Recently, Siemens Healthineers received Food and Drug Administration (FDA) clearance for TrueFusion, a cardiovascular application that integrates advanced ultrasound and angiographic imaging for improved navigation and guidance during structural heart disease interventions (19). This system integrates the coregistration of angiographic images and echo images into the workflow via ML-based probe detection and automated registration updates, enabling clinical teams to identify soft tissue–based structures that are provided directly from the integrated ultrasound system. TrueFusion can enable echocardiographers and interventionalists to better communicate and achieve more intuitive anatomical orientation during challenging procedures. This may result in reduced contrast usage, procedure time, and radiation exposure (19). In the future, via DL, automated diagnosis of imaging-based pathologies may be possible independent of an imaging specialist.
Clinical decision support
Clinical decision support systems with cognitive computing are under development and include self-learning systems using ML, pattern recognition, and NLP to mimic human thought processes (Figure 2). A multinational study, CEREBRIA-1 (Machine Learning vs Expert Human Opinion to Determine Physiologically Optimized Coronary Revascularization Strategies), evaluated whether an ML algorithm based on computational interpretation of pressure-wire pull back data would be similar to expert human interpretation for treatment strategies in patients with stable coronary artery disease (20). The study included 1,008 instantaneous wave-free ratio pullback traces, including 317 duplicates, which were analyzed by both the ML algorithm and a multinational team of interventionalists. They found that the computer-based ML program was noninferior to the expert consensus decision for both appropriateness of PCI and determining PCI strategy (20).
IBM’s Watson for Health applies cognitive technology to extract and analyze information from the EMR, lab reports, imaging reports, the published medical reports, guidelines, and various Internet sources (2,3). This technology combines ML and systems neuroscience to build powerful, general-purpose learning algorithms into neural networks that mimic the human brain. The IBM Watson for Oncology cognitive computing system can provide confidence-ranked, evidence-based treatment recommendations for cancer. Treatment recommendations by Watson and a tumor board were concordant in 96% of lung, 81% of colon, and 93% of rectal cancer cases (21). Currently, IBM is developing Medical Sieve, an automated cognitive assistant for cardiologists and radiologists designed to aid clinical decision-making. This IBM project has addressed many modalities of cardiac imaging including automatic detection of coronary stenoses in angiography.
Virtual Reality, Augmented Reality, and AI
Although there are technological differences in virtual reality (VR)/augmented reality (AR) and AI, combined application of these technologies may be useful for interventional procedures (Figure 2). VR platforms are currently being used in periprocedural planning of structural heart interventions, as well as pre-procedure patient experiences to decrease anxiety and stress (22). The FDA-approved True 3D system developed by EchoPixel (Santa Clara, California) renders patient-specific anatomy in an intuitive, interactive VR format (23). SentiAR Inc, a St. Louis, Missouri–based company, received a $2.2 million research grant from the National Institutes of Health to develop an AR cardiac hologram technology that allows real-time viewing, measurement and manipulation of patient anatomy in a holographic display for procedural guidance (24) (Figure 3). Similar AR systems can be used to overlay important information required during procedures that is typically displayed on multiple monitors stacked around the interventionist.
Voice-powered virtual assistants
Voice-powered virtual assistants, such as Apple’s Siri, Amazon’s Alexa, and Google’s Assistant, employ AI speech recognition that has now improved to the point of exceeding human accuracy in transcribing conversation. Compared with typing on keyboards for searching data in EMR or online medical information, voice is easier, faster, more convenient, and with the help of a unique voiceprint, can eliminate the need for passwords (25). Voice-powered virtual assistants use speech recognition and NLP to “understand” and process spoken data for output. AI empowered, voice-powered virtual assistants have the potential to process the input of multimodal data/images, and then present them in a meaningful way to the physician or operator. A voice-powered virtual assistant system in the catheterization laboratory could help operators control equipment, navigate the EMR system or access image libraries more effectively in a hands-free fashion.
“Big Data” Research and Predictive Analysis
“To date, big data, such as “omics” data, human gut microbiome sequencing, social media, and cardiac imaging, are too large and heterogeneous to be stored, analyzed, and used to their full potential” (2). AI techniques may solve this issue as well as allow for automatic generation of new hypotheses, instead of requiring that physicians postulate them (1,6). Traditionally, regression-based statistical methods are limited to using a small number of predictors, which operate in the same way on everyone with uniformity throughout their range. Moreover, interactions mediating physiological processes may be extremely complex to be captured using common regression techniques. Unsupervised DL may facilitate exploration of novel factors in score systems and better prediction analysis, or add hidden risk factors to existing models (2,7). This could lead to new models for antiplatelet/anticoagulant therapy, bleeding versus stroke risk, mortality risk with procedures, and so on (2,6). ML-based techniques also can highlight long-term outcomes or late complications for patients who have undergone a specific procedure or are prescribed a specific drug. Previously, ML has been used in cardiology to predict 1-year mortality in heart failure patients and 5-year mortality from coronary angiography datasets in patients with suspected coronary artery disease (4,7). Several recent studies have used ML-based techniques for advanced analysis of a dataset. Boone et al. (26) evaluated the cardiovascular disease profile of hematological malignancy patients undergoing PCI and used bivariable analysis with bootstrapping and result validation by an ML-generated neural network. A study by Ganim et al. (27) assessed racial disparities in procedural complications after transcatheter aortic valve replacement using ML-driven, backward propagation neural networks. Topological data analysis within an ML framework can be helpful in large volume multidimensional data interpretation, such as describing new phenotypes of diseases or combining imaging, clinical, and other nonstructured data.
Integration of AI-Based Decision-Making Across all Phases of Patient Care
In the future, interventional cardiologists will likely see AI analysis of population- and patient-specific data augmenting each phase of care. AI algorithms will be used in the emergency department to triage chest pain patients. In a pilot study, ML algorithms showed 94% accuracy for predicting a myocardial infarction in patients presenting with chest pain in the emergency department (28). Automated analysis of all pre-operative mobile and clinical data will provide patient-specific risk scores for procedural planning and yield valuable predictors to inform post-procedural care. Algorithms may be applied to patients with cardiogenic shock in order to determine who might benefit from mechanical circulatory support. DL prediction models will predict the periprocedural risk of death, bleeding, contrast nephropathy, and stroke (1,2). Predictive analytics with the use of cognitive computing may support clinical decision-making and help prioritize tasks in the catheterization laboratory. Intraprocedural monitoring of various data may lead to the real-time prediction and avoidance of adverse events. Integration of pre-, intra-, and post-procedural data could help to monitor recovery, predict complications, and recommend the optimal duration of medical therapy (2,5). After discharge, post-operative data from personal devices could continue to be integrated with data from the hospitalization to optimize patient recovery and decrease readmission rates. AI-enabled applications and apps may encourage and incentivize healthier behavior in individuals and may help with the preventive strategies of a healthy lifestyle and medication adherence (2).
Physical Application of AI
AI, through procedural automation, has the potential to increase the value of robotics in the catheterization lab by reducing the variability of procedure time and improving overall patient care (Figures 2 and 4). Although robotic interventions show promise, limitations and hurdles remain. Current systems do not comprehend the anatomy they display to the interventionalist, the procedures they are being used to perform, or know what the interventionalist intends to do. It is possible to enhance these robotic tools using AI technologies such as computer vision and image analysis (5). Although truly autonomous robotic vascular procedures will remain out of reach for some time, synergy across fields will likely accelerate the capabilities of AI in augmenting interventional care.
Corindus Vascular Robotics (Waltham, Massachusetts) recently received 510(k) clearance from the FDA for the first automated robotic movement designed for the CorPath GRX platform (29). The proprietary software feature, named “Rotate on Retract,” is an automated robotic movement that allows the operator to quickly navigate to a targeted lesion by automatically rotating the guidewire upon joystick retraction. Preclinical data demonstrated a significant reduction in wiring time among a highly experienced group of physicians when comparing robotic wiring versus robotic wiring with Rotate on Retract enabled. Future generations of vascular robotic platforms will be more aware of the procedures being performed and use that knowledge to provide intelligent assistance to interventional cardiologists. Companies such as Verb Surgical, a collaboration between Google and Ethicon Endo-Surgery, have indicated that their surgical robots will include ML and awareness, which will aim to identify potential issues during a procedure (30). They plan to link the robot to a cloud supercomputer service akin to IBM’s Watson, so that information on thousands of similar procedures will be accessible to both the surgeon and the robot to improve performance (5,30). Additional projects under development include the development of microbots that can travel through blood vessels to deliver medications to a specific target. Future applications of such microbots include the potential to repair damaged cells or perform microprocedures, which might include a variety of vascular interventions (1,5). Robots can be useful in interventional training simulator and teleintervention. Outside the catheterization lab, robots can help in post-procedural rehabilitation, and can be useful in other areas of health care such as pharmabotics, disinfectant robots, supply chain robots, and personal assistance (2,5,31). The true potential of AI in robotics remains to be seen and is difficult to predict at this time. Therefore, practicing interventional cardiologists should be engaged in assessing the quality and applicability of AI advances to ensure appropriate translation to the clinical sector.
Several organizations and startup companies are presently evaluating the application of AI-based technologies in other areas of health care that may influence IC. These include virtual nurses, digital consultation, medication management by patients, drug creation, health monitoring with wearable health trackers and health care system analysis (1,6,31).
Limitations of AI-Based Technologies
As with any novel technology, AI and its subspecialties are subjected to unrealistic expectations, which may lead to disappointment and disillusionment in the future (Figure 5). As noted by experts in the field, “ML is a natural extension of traditional approaches, not a magical device that can spin data into gold” (32). There are instances where traditional analytical methods can outperform ML or where the addition of ML does not improve results (2,5). For instance, Frizzell et al. (33) reported that a number of ML algorithms did not improve prediction of 30-day heart failure readmissions compared with more traditional prediction models. “As more control is ceded to algorithms, it is important to note that these new algorithmic decision-making tools come with no guarantees of fairness, equitability, or even veracity” (32). Issues likely to arise with the application of ML/DL on various datasets include: 1) issues with data integrity (just a newer version of the classic adage: “garbage in, garbage out”); 2) issues related to lack of diversity in training datasets; and 3) an impaired or absent ability to fully evaluate for methodological bias in analysis. Of particular concern is the latter issue as it relates to neural networks, which are based on a ‘‘black box’’ design (1,5). Although the automated nature of neural networks allows for detection of patterns missed by humans, human scientists are left with little ability to assess how or why the computer discerned such patterns. Human physicians, therefore, must critically evaluate the predictions generated by AI and interpret the data in clinically meaningful ways (1,8). Large, well-curated datasets are required for training of DL algorithms to provide diagnostic and predictive capabilities. However, the lack of large datasets of carefully annotated images and videos have been limiting across various disciplines in medicine including IC. Interestingly, generative adversarial networks have been used to compensate for this deficiency and synthetically produce large image datasets, including angiograms and echocardiograms, at high resolution that could be used to help train deep neural networks (34).
There are concerns that robotization may lead to an increase in unnecessary interventions, take focus away from patient expectations, or exacerbate existing socioeconomic biases related to care delivery. Wireless connectivity of wearable and implantable devices, cloud-based AI technologies, and robots face cyber security risks. A proposed solution to mitigate this issue is a custom-built “health care blockchain” technology that can store health information on an encrypted digital ledger in order to minimize cyber security risks (35).
One looming question is whether AI technologies could eventually replace a human interventional cardiologist. This does not look possible in the foreseeable future. Current technologies have several shortcomings in comparison to activities of the human brain. Structurally, bio-inspired neural networks resemble at best the outer layers of the retina or the visual cortex where images are just sensed or represented (36). The capacity of complex decision making or performing a procedure independently would be quite arduous for current AI/ML algorithms. AI, although widely publicized for its amazing performance, is actually quite shallow in intelligence in its current form (1,36). Robotic systems geared towards the technical performance of cardiovascular interventions are far too primitive to operate independently on patients. In the aviation industry, pilots embraced autopilot technology; however, most passengers would not want to do away with pilots. Similarly, we anticipate AI will assist rather than replace the human operator in the catheterization laboratory. The societal and ethical complexities of applications of AI require further reflection, proof of their medical utility, economic valuing, and development of interdisciplinary strategies for their wider application (37).
Synergizing IC and AI
There will always be a need for the expertise, compassion, and skill of a physician in the practice of IC. AI is not a replacement for human intellect. Rather, it has the potential to complement and reinforce it. The current practice of IC already represents a paradigm of clinician–machine interaction, and this synergy is likely to accelerate in the coming age of AI. Interventional cardiologists are uniquely positioned to help drive these innovations rather than passively wait for such technology to become useful. With AI-based systems providing more information for patient care, interventional cardiologists may see their roles shift. Physicians, including interventional cardiologists, need to develop additional skills, which machines cannot master in the near future, such as ethics, leadership, and empathy. Training programs must put a greater emphasis on how to optimize new AI tools. Important issues include posing the right questions to machines, interpreting their outputs, identifying when machines make mistakes, and integrating machine-generated data in patient care. Because lack of data can limit the predictions made by AI, physicians should seek to expand involvement in local, national, or international clinical data registries. As data cleaning techniques improve, registries could become linked to expand their utility and increase the availability of clinical, genomic, proteomic, radiographic, and angiographic data available for AI-based analysis. Interventionalists have the clinical insight that can guide data scientists and engineers to answer the right questions with the right data; whereas engineers can provide automated, computational solutions to data analytics problems that would otherwise be too costly or time-consuming for manual methods. If appropriately developed and implemented, AI has the potential to revolutionize the way IC is taught and practiced.
AI has initiated a paradigm shift in health care, powered by the increasing availability of health care data and rapid evolution of analytic techniques. It is expanding its footprint in clinical systems, including databases, image and intraprocedural video analysis, evidence-based, real-time clinical decision support, and robotics. The unique nature of interventional practice leaves the interventionalist well-positioned to help usher in the next phase of AI, one focused on synergistic interaction between man and machine, which ultimately will transform IC practice in efforts to improve clinical care.
Dr. Granada has received institutional grant/research support (to Skirball Center for Innovation) from Abbott Vascular, Amaranth Medical, Amber Medical, Amgen, Baylis, BIO2 Medical, Bristol-Myers Squibb, Boston Scientific, Cagent Vascular, Caliber Therapeutics, Cephea, Columbia Medical, Corindus Vascular, Celyad, Freudenberg Medical, Intact Vascular, JenaValve, Keystone Heart, LimFlow Medical, LoneStar Heart, Marvel Medical, Medtronic, Meril Life Sciences, MicroVention, Motus GI, Navigate Cardiac Structures, New York University, OrbusNeich Medical, SoundBite Medical, Spectranetics, Toray Industries, Vetex Medical, Volcano (Philips), and Zimmer Biomet. Dr. Giri has served on an advisory board for AstraZeneca; and has received research support to the institution from Recor Medical and Abbott Vascular. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- artificial intelligence
- augmented reality
- deep learning
- electronic medical record
- Food and Drug Administration
- interventional cardiology
- machine learning
- natural language processing
- percutaneous coronary intervention
- virtual reality
- Received August 8, 2018.
- Revision received February 26, 2019.
- Accepted April 2, 2019.
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- Central Illustration
- Concepts Underlying AI Methods
- Virtual Application of AI in IC
- Virtual Reality, Augmented Reality, and AI
- “Big Data” Research and Predictive Analysis
- Integration of AI-Based Decision-Making Across all Phases of Patient Care
- Physical Application of AI
- Limitations of AI-Based Technologies
- Synergizing IC and AI