Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a medical decision support system to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including normal). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technicians AR visual display

Medical Data Visual Synchronization and Information interaction Using Internet-based Graphics Rendering and Message-oriented Streaming

The rapid technology advances in medical devices make possible the generation of vast amounts of data, which contain massive quantities of diagnostic information. Interactively accessing and sharing the acquired data on the Internet is critically important in telemedicine. However, due to the lack of efficient algorithms and high computational cost, collaborative medical data exploration on the Internet is still a challenging task in clinical settings. Therefore, we develop a web-based medical image rendering and visual synchronization software platform, in which novel algorithms are created for parallel data computing and image feature enhancement, where Node.js and Socket.IO libraries are utilized to establish bidirectional connections between server and clients in real time. In addition, we design a new methodology to stream medical information among all connected users, whose identities and input messages can be automatically stored in database and extracted in web browsers. The presented software framework will provide multiple medical practitioners with immediate visual feedback and interactive information in applications such as collaborative therapy planning, distributed treatment, and remote clinical health care

Aspectos éticos da informática médica: princípios de uso e usuário apropriado de sistemas computacionais na atenção clínica

Medical Informatics (MI) studies the intersection among computer technology, medicine and the influence of electronic clinical history and the intelligent systems for diagnosis support in clinical decision making. The inadequate use of technology may divert the purposes of MI towards an inadequate use by third parties involved in clinical health care, such as health care managers or insurance agents. The principles for “use and appropriate user for MI applications” as base are proposed to manage suitably computational technology in health care. The development of these principles must be based in the evaluation of their applications, emphasizing that the evaluation must be carried out with the same considerations as other types of medical or surgical interventions.La Informática Médica (IM) estudia la intersección entre la tecnología computacional, la medicina y la influencia del uso de la historia clínica electrónica y los sistemas inteligentes de apoyo diagnóstico en la toma de decisiones clínicas. El uso inadecuado de la tecnología puede desviar los propósitos de la IM hacia su aprovechamiento impropio por terceros involucrados en la atención clínica, tales como administradores de salud o agentes aseguradores. Se plantea que los principios de “uso y usuario apropiado de la aplicaciones en IM” sean los fundamentos con los cuales se maneje adecuadamente la tecnología computacional en salud. El desarrollo de estos principios debe basarse en la evaluación de las propias aplicaciones, recalcando que ésta debe realizarse con las mismas consideraciones de otros tipos de intervenciones médicas o quirúrgicas.A Informática Médica (IM) estuda a interseção entre a tecnologia computacional, a medicina e a influência do uso da história clínica eletrônica e os sistemas inteligentes de apoio diagnóstico na tomada de decisões clínicas. O uso inadequado da tecnologia pode desviar os propósitos da IM para seu aproveitamento inadequado por terceiros envolvidos na atenção clínica, tais como administradores de saúde ou agentes de seguros. Propõe-se que os princípios de “uso e usuário apropriado das aplicações em IM” sejam os fundamentos com os quais se manipule adequadamente a tecnologia computacional em saúde. O desenvolvimento destes princípios deve se basear na avaliação das próprias aplicações, recalcando que esta se deve realizar com as mesmas considerações de outros tipos de intervenções médicas ou cirúrgicas

The health care sector’s experience of blockchain:a cross-disciplinary investigation of its real transformative potential

Background:Academic literature highlights blockchain’s potential to transform health care, particularly by seamlessly and securely integrating existing data silos while enabling patients to exercise automated, fine-grained control over access to their electronic health records. However, no serious scholarly attempt has been made to assess how these technologies have in fact been applied to real-world health care contexts.Objective:The primary aim of this paper is to assess whether blockchain’s theoretical potential to deliver transformative benefits to health care is likely to become a reality by undertaking a critical investigation of the health care sector’s actual experience of blockchain technologies to date.Methods:This mixed methods study entailed a series of iterative, in-depth, theoretically oriented, desk-based investigations and 2 focus group investigations. It builds on the findings of a companion research study documenting real-world engagement with blockchain technologies in health care. Data were sourced from academic and gray literature from multiple disciplinary perspectives concerned with the configuration, design, and functionality of blockchain technologies. The analysis proceeded in 3 stages. First, it undertook a qualitative investigation of observed patterns of blockchain for health care engagement to identify the application domains, data-sharing problems, and the challenges encountered to date. Second, it critically compared these experiences with claims about blockchain’s potential benefits in health care. Third, it developed a theoretical account of challenges that arise in implementing blockchain in health care contexts, thus providing a firmer foundation for appraising its future prospects in health care.Results:Health care organizations have actively experimented with blockchain technologies since 2016 and have demonstrated proof of concept for several applications (use cases) primarily concerned with administrative data and to facilitate medical research by enabling algorithmic models to be trained on multiple disparately located sets of patient data in a secure, privacy-preserving manner. However, blockchain technology is yet to be implemented at scale in health care, remaining largely in its infancy. These early experiences have demonstrated blockchain’s potential to generate meaningful value to health care by facilitating data sharing between organizations in circumstances where computational trust can overcome a lack of social trust that might otherwise prevent valuable cooperation. Although there are genuine prospects of using blockchain to bring about positive transformations in health care, the successful development of blockchain for health care applications faces a number of very significant, multidimensional, and highly complex challenges. Early experience suggests that blockchain is unlikely to rapidly and radically revolutionize health care.Conclusions:The successful development of blockchain for health care applications faces numerous significant, multidimensional, and complex challenges that will not be easily overcome, suggesting that blockchain technologies are unlikely to revolutionize health care in the near future

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Professional Judgment in an Era of Artificial Intelligence and Machine Learning

Though artificial intelligence (AI) in healthcare and education now accomplishes diverse tasks, there are two features that tend to unite the information processing behind efforts to substitute it for professionals in these fields: reductionism and functionalism. True believers in substitutive automation tend to model work in human services by reducing the professional role to a set of behaviors initiated by some stimulus, which are intended to accomplish some predetermined goal, or maximize some measure of well-being. However, true professional judgment hinges on a way of knowing the world that is at odds with the epistemology of substitutive automation. Instead of reductionism, an encompassing holism is a hallmark of professional practice—an ability to integrate facts and values, the demands of the particular case and prerogatives of society, and the delicate balance between mission and margin. Any presently plausible vision of substituting AI for education and health-care professionals would necessitate a corrosive reductionism. The only way these sectors can progress is to maintain, at their core, autonomous professionals capable of carefully intermediating between technology and the patients it would help treat, or the students it would help learn