Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry

Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence

The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates’ innate immunity

Shape memory polymer review for flexible artificial intelligence materials of biomedical

The self-healing and biocompatibility of polymer composites for biomedicine have made them a preferred approach for small-scale tissue engineering elements. By moving from static to dynamic pressure, 4D printing simulates the natural physical-mechanical changes of living tissue over time. A promising new platform with excellent controllability actuation is required to enhance the significance of 4D printing for biological applications. This study systematically analyses current 4D printing technologies for the flexible fabrication of artificial intelligence (AIM) materials. In addition, many potential applications of flexible 4D printing in composite biological engineering are thoroughly investigated. We found that knowledge about this new category of flexible AIM composites is relatively limited, and the potential for practical applications has not yet been demonstrated. Finally, we discuss the problems and limitations of flexible 4D printing technology, AIM, and future approaches and applications.</p

A manifesto on explainability for artificial intelligence in medicine.

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine

Industry 4.0:use of digitalization in healthcare

The primary objective of this chapter is to examine the AI applications for healthcare 4.0. Using a wide range of contemporary technologies, such as digitization, artificial intelligence, user response data (ergonomics), human psychology, the internet of things, machine learning, big data mining, and augmented reality, one of the great success stories of our day is healthcare. Worldwide life expectancy has increased due to the tremendous advancements in medical research. But when people live longer, healthcare systems must deal with more people needing their services, more money spent on them, and a staff that finds it more challenging to care for patients. A healthy, productive society depends heavily on the healthcare industry, making it one of the most critical industries in the larger big data environment. Artificial intelligence (AI), which builds on automation, has the potential to transform healthcare and assist in addressing some of the issues mentioned above. AI can support healthcare professionals, including physicians and nurses, in their day-to-day jobs. Artificial intelligence (AI) can improve patient outcomes by enhancing the quality of life and preventive care and producing more accurate diagnoses and treatment regimens. This book provides an overview of the most recent advancements in artificial intelligence (AI) applications in biomedicine, encompassing pharmaceutical processing, disease diagnosis, patient monitoring, biomedical information, and biomedical research. A summary of the most recent developments in the use of AI in healthcare is also provided, along with a road map for creating safe, dependable, and efficient AI systems and a discussion of potential future directions for AI-assisted healthcare systems. Numerous uses of AI exist in the medical field. Healthcare 4.0 has brought about a paradigm shift in the healthcare industry, drawing inspiration from Industry 4.0. Therefore, how the digital revolution in healthcare will affect the quality of medical care is still being determined. This study results will help the new researchers and healthcare institutions

The Artificial Intelligence Workbench: a retrospective review

Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time