scholarly journals Special Issue on Clinical Medicine for Healthcare and Sustainability

2020 ◽  
Vol 9 (7) ◽  
pp. 2206
Author(s):  
Teen-Hang Meen ◽  
Yusuke Matsumoto ◽  
Kuan-Han Lee
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Biomedical Engineering ◽  
Clinical Medicine ◽  
Healthcare Systems ◽  
Network Technology ◽  
Healthcare Industry ◽  
Special Issue ◽  
Scientific Prediction ◽  
Basic Medical

Recently, due to the advancement of network technology, big data and artificial intelligence, the healthcare industry has undergone many sector-wide changes. Medical care has not only changed from passive and hospital-centric to preventative and personalized, but also from disease-centric to health-centric. Healthcare systems and basic medical research are becoming more intelligent and being implemented in biomedical engineering. This Special Issue on “Clinical Medicine for Healthcare and Sustainability” selected 30 excellent papers from 160 papers presented in IEEE ECBIOS 2019 on the topic of clinical medicine for healthcare and sustainability. Our purpose is to encourage scientists to propose their experiments and theoretical researches to facilitate the scientific prediction and influential assessment of global change and development.

scholarly journals Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research

2018 ◽  
Vol 123 (12) ◽  
pp. 1282-1284 ◽  
Author(s):  
Fatima Rodriguez ◽  
David Scheinker ◽  
Robert A. Harrington
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Biomedical Research ◽  
Clinical Medicine

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

scholarly journals Big Data and the Threat to Moral Responsibility in Healthcare

2021 ◽  
pp. 11-25
Author(s):  
Daniel W. Tigard
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Moral Responsibility ◽  
Intelligent Systems ◽  
Healthcare Systems ◽  
Ethical Challenges ◽  
Trade Offs ◽  
The Social ◽  
Healthcare Technologies

AbstractTechnological innovations in healthcare, perhaps now more than ever, are posing decisive opportunities for improvements in diagnostics, treatment, and overall quality of life. The use of artificial intelligence and big data processing, in particular, stands to revolutionize healthcare systems as we once knew them. But what effect do these technologies have on human agency and moral responsibility in healthcare? How can patients, practitioners, and the general public best respond to potential obscurities in responsibility? In this paper, I investigate the social and ethical challenges arising with newfound medical technologies, specifically the ways in which artificially intelligent systems may be threatening moral responsibility in the delivery of healthcare. I argue that if our ability to locate responsibility becomes threatened, we are left with a difficult choice of trade-offs. In short, it might seem that we should exercise extreme caution or even restraint in our use of state-of-the-art systems, but thereby lose out on such benefits as improved quality of care. Alternatively, we could embrace novel healthcare technologies but in doing so we might need to loosen our commitment to locating moral responsibility when patients come to harm; for even if harms are fewer – say, as a result of data-driven diagnostics – it may be unclear who or what is responsible when things go wrong. What is clear, at least, is that the shift toward artificial intelligence and big data calls for significant revisions in expectations on how, if at all, we might locate notions of responsibility in emerging models of healthcare.

Patentability challenges associated with emerging pharmaceutical technologies

2021 ◽  
Author(s):  
Srishti Aggarwal ◽  
Amrish Chandra
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Stem Cell ◽  
Personalized Medicine ◽  
Cell Therapy ◽  
Emerging Technologies ◽  
Healthcare Industry ◽  
Biological Therapies ◽  
Patent Laws ◽  
Patent Filing

According to the recent patent filing trends, it has been observed that certain pharmaceutical technologies are more popular than others and are commonly referred to as emerging technologies. The emerging technologies in the pharmaceutical sector include artificial intelligence, big data and certain advanced biological therapies such as personalized medicine and stem cell therapy. These trends have various applications in the medicine and healthcare industry. Since these technologies are relatively new and each of them is very unique in its own way, current patent laws are inadequate to deal with the complex challenges associated with them. A brief analysis of the challenges associated with these emerging technologies and their applications is discussed in this paper.

AI-Driven Big Healthcare Analytics

2021 ◽  
pp. 172-184
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Centers ◽  
Medical Data ◽  
Healthcare Industry ◽  
Healthcare Organizations ◽  
Healthcare Analytics ◽  
Digital Contents ◽  
Patients At Risk ◽  
Medical Big Data

With the increased development of technology in healthcare, a huge amount of data is collected from healthcare organizations and stored in distributed medical data centers. In this context, such data quantities, called medical big data, which include different types of digital contents such as text, image, and video, have become an interesting topic tending to change the way we describe, manage, process, analyze, and visualize data in healthcare industry. Artificial intelligence (AI) is one of the sub-fields of computer science enabling us to analyze and solve more complex problems in many areas, including healthcare. AI-driven big healthcare analytics have the potential to predict patients at risk, spread of viruses like SARS-CoV-2, spread of new coronavirus, diseases, and new potential drugs. This chapter presents the AI-driven big healthcare analytics as well as discusses the benefits and the challenges. It is expected that the chapter helps researchers and practitioners to apply AI and big data to improve healthcare.

An Experimental Healthcare System

2021 ◽  
pp. 301-308
Author(s):  
Miroslav M. Bojović ◽  
Veljko Milutinović ◽  
Dragan Bojić ◽  
Nenad Korolija
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Knowledge Management ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Clinical Guidelines ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Starting Point ◽  
Data Architecture

Contemporary healthcare systems face growing demand for their services, rising costs, and a workforce. Artificial intelligence has the potential to transform how care is delivered and to help meet the challenges. Recent healthcare systems have been focused on using knowledge management and AI. The proposed solution is to reach explainable and causal AI by combining the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion in a way that is accessible and understandable to physicians. Therefore, the authors propose AI approach in which the encoded clinical guidelines and protocols provide a starting point augmented by models that learn from data. The new structure of electronic health records that connects data from wearables and genomics data and innovative extensible big data architecture appropriate for this AI concept is proposed. Consequently, the proposed technology may drastically decrease the need for expensive software and hopefully eliminates the need to do diagnostics in expensive institutions.

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