Is the Cardiovascular Specialist Ready For the Fifth Revolution? The Role of Artificial Intelligence, Machine Learning, Big Data Analysis, Intelligent Swarming, and Knowledge-Centered Service on the Future of Global Cardiovascular Healthcare Delivery

Abstract

John J. Bergan 1 famously quoted—In vascular surgery, no change for the better has occurred that wise and good men have not opposed. Such change is inevitable. Historically, the first revolution in vascular surgery was by Alexis Carrel, 2 who won the Nobel Prize in physiology/medicine in 1912 for suturing of blood vessels and transplantation of organs. However, it took almost 45 years for the second vascular revolution to occur, which involved 2 of the world’s most outstanding cardiovascular surgeons—Dr Michael E. DeBakey and Dr Denton A. Cooley. 3 Both pioneered cardiovascular operations at the Baylor College of Medicine and Methodist Hospital in Houston in the 1950s that have stood the test of time. During that time, Charles Theodore Dotter, the father of intervention radiology, accidentally performed a vascular dilatation in 1963, followed by intentional percutaneous angioplasty by a balloon in 1964, to set the stage for the third vascular revolution of angioplasty and the role of interventional radiology. 4 In September 1990, the fourth vascular revolution was brewing in Argentina, as Dr Parodi, helped by Julio Palmaz and Hector Barone, performed the first reported endovascular aortic aneurysm repair (EVAR) under epidural anesthesia. 4 The human capabilities that are most critical to success—the only ones that help your organization become more resilient, more creative and more, well, awesome—are precisely the ones that can’t be “managed”.—Whitehurst and Gary Hamel. 5 Vascular evolution has shown that change is imminent, largely driven by inefficiencies in the provision of healthcare services, rising costs and growing levels of litigation. Technologies that improve productivity and enhance safety will become an integral part of healthcare delivery in modern medicine. However, many of us are yet reluctant to encompass a shift in technology and step beyond the comfort of traditional practice. According to the World Economic Forum report, one-third of essential skillsets in 2025 will comprise technological skills not yet considered imperative to the job today. 6 Evolution in technology will shape the future of medicine. Based on this trend, we can predict that the cardiovascular global workforce will undergo a massive technological shift leading to the “Fifth Cardiovascular Revolution.” Knowledge and proficiency in technology will be an integral part of the skillsets required for clinical practice, and beyond doubt, artificial intelligence (AI), machine learning (ML), and Big Data analysis (BDA) will transform global healthcare practice. The evolution of these technologies will have profound implications on our workforce beyond anyone’s comprehension. 6,7 Fewer organizations will fill these critical roles with skillsets that will evolve and emerge from the Fifth Cardiovascular Revolution. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Big Data Analysis (BDA) Artificial intelligence is the exhibition of human intelligence through machines required for problem-solving. 8 Machine learning is the subset of AI that allows computers to learn and automate through computational algorithms without human intervention. In this context, DL is a subset of ML that enables machines to train and learn through an artificial neural network. Similarly, NLP is the branch of AI that helps computers understand human language, whereas BDA involves the analysis and extraction of information from a large volume of data obtained through various sources. Advances in computing power, data storage capacities and hardware acceleration allowed the storage of large volumes of medical data in hospitals and clinical data centers. This presents an excellent opportunity to exploit AI and BDA as well as the technological advances within ML, DL, and NLP fields to facilitate, improve, and support medical decisions. This will allow us to exploit the diversity of medical and healthcare big data to perform complicated tasks with precision and accuracy. 9 The digitization and availability of these data sets have encouraged many researchers to explore various ML, DL, and NLP techniques to support healthcare applications. Among the applications in healthcare is to help and assist in medical diagnosis and outcome prediction. However, there are endless opportunities for AI and BDA to support, improve, and even revolutionize the healthcare industry. These fast-growing technologies can enhance diagnostics and care delivery, improve patient–doctor communication, and smoothen hospital administration and community health management with AI-powered healthcare solutions. Big data from diverse sources and medical systems can be fed to AI algorithms, for example, to build interfaces for medical software and devices to create intelligent applications for optical character recognition. Currently, the major beneficiaries of the 21st century advances in big data, ML, and data science are global industries and financial sectors because they have the resources to hire the necessary staff to transform their products and acquire the knowledge to be ahead of the game. However, the algorithms developed for these industries offer considerable opportunities to further clinical care and medical research. Implications of AI in Cardiovascular Medicine As cardiovascular specialists and key opinion leaders, we must prepare to meet the fast-growing technological demands and beyond. We must develop, deliver and learn how to survive the technology dominating future as innovators and early adaptors, and lead through the “Fifth Cardiovascular Revolution.” 10,11 Artificial intelligenceI, ML, and BDA in healthcare could drive real value and transform care delivery for patients and caregivers, raising the bar for a new gold standard that empowers patients to experience personalized and frictionless healthcare journeys. The application of AI and BDA to a cardiovascular health strategy offer limitless opportunities as cardiovascular disease is the commonest cause of morbidity and mortality worldwide. The current results promise to provide a continuous source of data-driven insights to optimize biomedical research, precision imaging, early public health primary intervention, and overall superior healthcare quality improvement. 12 One major goal in the application of AI and BDA is to accurately predict cardiovascular morbidity and mortality over time. One promising avenue to explore; is to employ AI and BDA to identify patients at high-risk for cardiovascular medical emergencies; in particular, AI-powered technologies could empower us to accurately predict when the disease will occur, and once the disease is evident, we can predict when a patient will relapse or transit into another disease state. Other promising applications include the integration of image and language processing so that images of various pathologies can be mapped and radiomics can be used to consolidate visual and textual data to understand relevant disease features. This can allow for more precise predictions. There is a growing area of research in “Multimodal Data Analysis,” which is focused on the integration and interpretation of diverse data of various types and modalities. 13 This allows the AI techniques and their accompanying technologies of ML, DL, and NLP to learn the relationship between many complex data, such as demographics, risk factors, genomics, proteomics, and radiomics. 14 Healthcare systems have a lot to learn from other industries that employ best practices such as “Six Sigma” to build robust AI algorithmic solutions to enhance efficiency and provide seamless workflows. 15 Through AI and BDA applications, the potential exists to reinforce our expertise in supervised and unsupervised cardiovascular practices with AI and BDA skills and knowledge, to understand the use of the state-of-the-art ML, neural networks, and DL approaches, and to build intelligent systems that make better decisions with the minimum human help possible. An exciting avenue that we strongly recommend is employing supervised ML and DL techniques and algorithms to develop taxonomies from data sets containing multiple heterogeneous inputs. Supervised ML and a data set containing multiple heterogeneous inputs require 3 levels of algorithms. The first algorithms accurately manage the “high sparsity data sets” and fill the missing entry points by properly using its regularization function. The second algorithm is a mathematical high-performance “kernel trick” as a support vector machine that derives accurate predictions in situations where the relationship between features and the outcome is nonlinear. Finally, the third algorithm is an artificial neural network with complex architecture and heavily modifiable parameters that led to the widespread use of high-definition clinical imaging and video recognition in many challenging applications. 14 Integrating AI and BDA into the cardiovascular field will allow medical staff to devote more time to caring for patients rather than doing other tasks that AI algorithms can execute with precision. Such tasks include (1) checking results continuously, (2) alarming physicians with changes in baselines, and (3) following agreed protocols for emergency intervention. This will allow for neutralizing human error and creating a robust, experienced system that can handle any catastrophe at any given time with accuracy. Continuous monitoring systems that employ AI algorithms to recognize abnormalities in patients’ clinical status offer great potential to ensure timely and life-saving intervention. Various features are used in such algorithms, including patients’ clinical signs and symptoms, medical laboratory investigations, high-definition imaging modalities, and haemodynamic and clinical data activity feeds. 16 The field of radiomics offers fascinating potential for cardiovascular diagnostics. It is a promising approach for characterizing imaging through morphological and functional imaging data to enhance diagnostics and treatment. Radiomics could bring order out of chaos by translating data sources that are spatially and temporally heterogeneous to organized data through ML algorithms. 17,18 The European Society of Radiology (ESR) 19 performed a survey to determine the radiologists’ position toward AI and BDA technologies. The survey exposed the shocking results that radiologists do not consider the implementation of AI systems into radiological diagnostics as a probable field for AI application in the next 10 years. 19 Half of the radiologists foresaw a negative impact of AI systems on future job opportunities with fear that technological advancements could lead to a nearly two-thirds decrease in their workload. However, what those surveys failed to realize is that AI and BDA reduce time working on diagnostics and thereby free up time to provide stronger interactions and increased communication with other clinicians. It is expected that radiologists who adopt AI and BDA technologies will replace radiologists who do not. 20 On a positive note, AI-based algorithms can detect major cardiovascular diseases in 60% of asymptomatic subjects. 21,22 Moreover, such algorithms can be an alternative to conventional image processing methodologies for image segmentation, registration, classification, and enhancement, making DL-based approaches the best tactics for computer vision and medical image processing. 21,22 These successful examples allow us to move forward from theory to the clinical cardiovascular practice of ML for clinicians and medical researchers. The basic principle is to validate such complex tasks, emphasizing NLP and image recognition. 14 It is essential to understand that with time, the application of AI, intelligent swarming, and knowledge-centered services (KCSs) will grow exponentially in cardiovascular medicine, and the significant economic burden associated with its initial implementation will decrease not only over time but also the return on investment will be worth the initial capital buy-in (Figure 1). Figure 1. Predicted rise in the application of AI, ML, BDA, and related technologies in cardiovascular medicine with a decrement in the application cost over time. AI, artificial intelligence; ML, machine learning; BDA, Big Data analysis. Intelligent Swarming Intelligent swarming is the concept of using the required resources to solve the problems without escalations to a higher level (Figure 2). 23 Figure 2. New model swarming with the proposed collaboration-based process during intelligent swarming. Intelligent swarming supports a collaborative work environment and prevents significant time-lapses, providing speedy healthcare delivery. For example, to improve collaboration with the complex issues of trauma patients, as an example, from level 1 trauma to level 3 trauma, health organizational silos have resulted in competition and scapegoating between departments (Figure 3). The support issues are complex, and level 1 trauma-support members often cannot solve problems in isolation. With a lack of communication and failure to share data, patient care can be inappropriately escalated within or between emergency departments. This is a clear example of where risks to patients can arise, whereby clinical signs get missed, unwarranted transfers are enacted, and lives are lost. Intelligent swarming has the potential to solve these issues. Figure 3. Traditional escalation process that is time-consuming and labor intensive. If we employ intelligent swarming to address healthcare delivery issues, we can save precious time and reduce costs by bypassing traditional hierarchical and obstructive healthcare delivery systems. Healthcare providers have been insidiously considering intelligent swarming or “collective intelligence” concepts for many years. However, its ultimate implementation is dependent on the members’ willingness and courage to try something that has not been done before, as the elements of the framework are still in a discovery phase. 24,25 Implementing intelligent swarming includes the power of collective thinking and collective experience. Knowledge-Centered Service (KCS) A KCS is about treating knowledge as power and a business asset. 26 Early benefits of KCS are improved resolution times and first call resolution, reduced escalations, improved employee skills, job satisfaction, and confidence. These benefits translate into less stress and potentially better retention rates, dramatic improvement in self-service success, and reduced training time. Similarly, long-term benefits include identification of user behaviors and trends, improved location of resolutions via AI-driven solutions, and better analytical predictive and pre-emptive abilities that enable proactive clinical engagement. The successful implementation of KCS enables organizations to improve their ability to capitalize on their collective knowledge and wisdom. A KCS increases efficiency as team members spend less time searching for information and more on helping patients and maintaining a sound health administration. A visible store of knowledge will identify early that something has gone off base, predict adverse events before they occur and inform how to correct them quickly. It makes the unknown known. Knowledge-centered service opposes the traditional knowledge engineering approach, which was designed to capture the minimal data necessary to permit the timely transmission of knowledge to many people. The 5 principles of KCS include—(1) create abundance (share and learn more), (2) evolve and collective experience (fine-tune the work tasks and increase the knowledge base), (3) demand-driven and reuse (knowledge is a by-product of interaction and double-loop feedback), (4) resolve (engage all task holders and empower the smart), and (5) improve and reward learning (to motivate all comers). Knowledge-centered service is a many-to-many model through extensive data analysis, deep ML, and AI algorithms (Figure 4). Figure 4. Principle of KCS. KCS, knowledge-centered services. Limitations and Opportunities The best way to predict the future is to invent it—Alan Key. 27 There is a lack of basic AI knowledge or an understanding of AI principles and terminology. Furthermore, there is a paucity of skills and confidence in the use and application of AI solutions. 28 Therefore, we have a stronger need for formalized education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare to safely and efficiently navigate a digital future. As innovative and early adaptors, we must concentrate on the needs of early learners depending on their age, sex, and highest qualification to ensure optimal integration. Transforming workforce productivity is an important area where AI delivers excellent value through automation. There is plenty of hype, jargon, and abstract technology, making it difficult for nonexperts to identify the most exciting opportunities to apply AI into everyday practice. Learning the basics of AI and its associated technologies, such as ML, DL, NLP, and BDA, begins with the terminology’s proper understanding. Undoubtedly, the future will dictate that the knowledge of these technologies is essential and that their applications will be crucial for healthcare practitioners. Artificial intelligence–associated technologies will result in the computerization of jobs with a substantial displacement of the human workforce. A Deloitte collaboration with the Oxford Martin Institute predicted that AI could push 35% of U.K. jobs out of existence over the next 10 years. 29,30 During the COVID-19 pandemic, working from home imposed enormous challenges on the medical sectors, where many of us operated virtually to avoid the risk of spreading COVID. As a result, this created specific new challenges such as ensuring reliable internet connections and high-quality video calls. Thus, there is a pressing need to solve such challenging problems by automating around 60% of all interactions by 2024 using AI-powered systems and self-services (Figure 5). 31,32 Figure 5. Increasing applicability of artificial intelligence in cardiovascular medicine —25% in 2018 to 60% in 2024. The application of AI to healthcare will not be seamless. Artificial intelligence researchers face many challenges during the design and development of AI-, ML-, or DL-based algorithms, especially in healthcare. Among these challenges are data set shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalization to new populations, and the inadvertent negative consequences of new algorithms on health outcomes. 31 –33 These challenges could be neutralized by employing and adapting performance metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient-driven/oriented outcomes. Timely and safe AI translation into clinically validated and robust regulated systems will benefit everyone. Regulators must balance the pace of innovation with the potential for harm alongside thoughtful postmarket surveillance. This ensures that patients are not endangered by any AI interventions, nor are they denied access to advantageous innovations. 31 –34 The purpose of AI is not about replacing humans’ tasks with machines; it is about changing the routine work usually carried out by humans who can focus on more purposeful work and optimize workplace productivity. One of the existing challenges in rendering AI algorithms friendly to clinical practice is the lack of harmonized accessible data. Most healthcare data are not readily available for ML algorithms. Data are often buried in several medical imaging archival systems, pathology systems, electronic health records (EHRs), prescribing tools and insurance databases, which are challenging to bring together. 35 –37 Ethical Implications We are at the kindergarten stage of the “Fifth revolution.” Considering the medico-legal implications of AI outcomes, there is potential for critical and controversial issues that are not yet clearly defined. Artificial intelligence–based system accountability represents a challenge that will require robust regulations. 38 In the past, humans have made healthcare assessments singularly. Although intelligent machines provide collective decision-making, it also raises issues like accountability, transparency, permission, and privacy. Many AI algorithms lack transparency, particularly for DL and ML-based algorithms such as those used for image analysis, as it is practically impossible to interpret or explain. If a patient is informed that an image has led to an impending medical problem, the patient will more than likely want to know why. Deep learning algorithms, and even physicians who are generally unfamiliar with their operation, may be unable to provide any explanation. Moreover, future patients will likely receive medical information from AI-based systems that they would prefer to receive from an empathetic clinician face to face. 39 Robust clinical governance must act responsibly and establish governance mechanisms to limit negative implications. Artificial intelligence and BDA are potent tools, and their powerful technologies will impact human societies. It will require continuous attention and thoughtful policy for many years to come. 40 We must better understand the complex and evolving relationship between clinicians and human-centered AI tools in an evolving clinical environment. 41 –45 It is still challenging to provide diagnosis and treatment recommendations through AI-based systems; however, we expect AI will ultimately master that domain. Given the rapid advances in image analysis, it is most likely to impact the field of medicine—radiology and pathology first, leading to images being solely examined by a machine. We can foresee that AI and BDA technologies will extensively influence our cardiovascular clinical practice within 5 years and augment patient care efforts. Over time, human clinicians will gear toward job plans and tasks that allow for uniquely human skills like empathy, persuasion, and integration of the bigger picture. 40 Healthcare providers who will not join the fifth cardiovascular revolution by embracing AI programs will ultimately lose their jobs over time. Artificial intelligence and BDA will improve scarce healthcare resources with personalized precision patient management plans. It will inform and impact policies and guidelines. It will also expedite clinical trials. 46 Furthermore, AI will create a paradigm shift in “disease management,” from providers waiting for individuals to become sick and present with symptoms and signs to healthcare organizations increasingly focused on disease prevention by proactively monitoring healthy individuals, performing preventative and wellness interventions, and managing prevention and wellness for at-risk individuals. 47 –49 Current medical education is antiquated; AI and BDA will pioneer a new division in medicine. This professional hybrid physician, part-computer engineer and data scientist, will increasingly embark on new roles and shift from “oracle” to “counsellor” to the “COMENDA” physiCian, cOMputer ENgineer, DAta scientist. In this AI and BDA—empowered workforce, the proficiency to decipher clinical problems together will become more important than the knowledge of each practitioner. 50 –52 Salient Point Deciphering AI/ML/BDA will grant clinicians and researchers without previous experience the ability to critically scrutinize these techniques. It is mandatory to understand the way algorithms are created, as medical practitioners will rely heavily on such contemporary technology, which might not always perform as expected. Currently, models are unable to achieve perfect performance. The legend of the Google Flu Trends model failure offers stark scrutiny of consequences mitigated by an inability to understand AI/ML/BDA models, which was employed and implemented superficially to improve Google health trends. 53 Machine language algorithms training is mandatory to abolish and lessen the risks of entrenching biases in predictive algorithms as medical practice bias-based risks have been constantly acknowledged. If left unchecked, it jeopardizes the ethical use of data-driven automation. 54 Conclusion Artificial intelligence/ML/BDA momentum will transform and disrupt how medicine works. Thus far, the enthusiasm has not been met by the ease of access to clinician training targeted to the knowledge and skillsets required of any medical practitioner to deal with such disruptive technology. It is prudent to equalize the need for AI/ML/BDA plans that actively manages and lessens potential accidental consequences while not conceding to marketing hype and profit motives. Artificial intelligence–, ML-, and DL-based approaches are the “payback” for the investment in the implementation of EHRs. Electronic health records have provided support for extensive data collection that every clinician, patient, and family would want, but exploiting these data for healthcare provision is impossible without computer base assistance. Artificial intelligence and BDA will empower our healthcare systems to monitor patients remotely and intercept emergent clinical scenarios. The creation of the AI-empowered healthcare specialist is crucial for a brighter future.