IMPACT OF ARTIFICIAL INTELLIGENCE ON AGRICULTURAL, HEALTHCARE AND LOGISTICS INDUSTRIES

This qualitative research study was conducted to illustrate the relationships between Artificial Intelligence (AI) and non-tech businesses. AI is a broad branch of computer science. In information technology, the intelligent machine is a compliant and logical agent that recognizes its environment and takes full advantage of opportunities to achieve something. This paper provides detailed examples using AI outside of IT. The main method which is used for this research is literary analysis. The article consists of explanations about artificial intelligence in general, its impacts on logistics and transportation, agriculture and healthcare industries. Moreover, in this article, the methods used to leverage the developments of aforementioned industries are also mentioned and discussed.

COMPUTERIZED REASONING AND ITS APPLICATION IN DIFFERENT AREAS

Later on, intelligent machines will supplant or improve human abilities in numerous ranges. Manmade brainpower is the insight displayed by machines or programming. It is the subfield of software engineering. Counterfeit consciousness is turning into a prevalent field in software engineering as it has improved the human life in numerous regions. Counterfeit consciousness over the most recent two decades has extraordinarily enhanced execution of the assembling and administration frameworks. Concentrate in the territory of manmade brainpower has offered ascend to the quickly developing innovation known as master framework. Application zones of Artificial Intelligence is huge affecting different fields of life as master framework is broadly utilized nowadays to take care of the perplexing issues in different ranges as science, building, business, solution, climate estimating. The territories utilizing the innovation of Artificial Intelligence have seen an expansion in the quality and proficiency. This paper gives an outline of this innovation and the application regions of this innovation. This paper will likewise investigate the present utilization of Artificial Intelligence advances in the PSS configuration to clammy the power framework motions caused by interferences, in Network Intrusion for shielding PC and correspondence systems from gatecrashers, in the therapeutic region prescription, to enhance healing facility inpatient mind, for restorative picture arrangement, in the bookkeeping databases to alleviate its issues and in the PC recreations

Can nurses remain relevant in a technologically advanced future?

Technological breakthroughs occur at an ever-increasing rate thereby revolutionizing human health and wellness care. Technological advancements have drastically changed the structure and organization of the healthcare industry. McKinsey Global Institute estimates that 800 million workers worldwide could be replaced by robots by the year 2030. There is already a robotic revolution happening in healthcare wherein robots have made tasks and procedures more efficient and safer. Locsin and Ito has addressed the threat to nursing practice with human nurses being replaced by humanoid robots. Routine nursing care dictated solely by prescribed procedures and accomplishment of nursing tasks would be best performed by machines. With the future practice of nursing in a technologically advanced future transcending the implementation of nursing actions to achieve predictable outcomes, how can human nurses remain relevant as practitioners of nursing? Nurses should be involved in deciding which aspects of their practice can be delegated to technology. Nurses should oversee the introduction of automated technology and artificial intelligence ensuring their practice to be more about the universal aspects of human care continuing under a novel system. Nursing education and nursing research will change to encompass a differentiated demand for professional nursing practice with, and not for, robots in healthcare

Health Care Using AI

Breast cancer treatment is being transformed by artificial intelligence (AI). Nevertheless, most scientists, engineers, and physicians aren't ready to contribute to the healthcare AI revolution. In this paper, we discuss our experiences teaching a new American student undergraduate course that seeks to train the next generation for cross-cultural design thinking, which we believe is critical for AI to realize its full potential in breast cancer treatment. The main tasks of this course are preparing, performing and translating interviews with healthcare professionals from both Portugal and the USA. Since the course is offered in Portugal as a short-term faculty-led study abroad program, students can explore the effect of culture on healthcare delivery and the design of healthcare technologies. The learning tests demonstrated student growth for breast cancer treatment in many areas important for the development of AI. In respect to understanding breast cancer care, most students had undervalued the effect of cancer and its treatment on the quality of life of women before taking this course and most were unaware of the importance of multidisciplinary care teams. Regarding AI in medical, students became more mindful of data privacy issues and the need to consider the effect of AI on healthcare professionals

Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level

"Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high, with experts believing that AI paves the way for managing and handling various global challenges. However, the significant enabling and inhibiting influence of AI for sustainable development needs to be assessed carefully, given that the technology diffuses rapidly and affects millions of people worldwide on a day-to-day basis. To address this challenge, a panel discussion was organized by the KTH Royal Institute of Technology, the AI Sustainability Center and MIT Massachusetts Institute of Technology, gathering a wide range of AI experts. This paper summarizes the insights from the panel discussion around the following themes: The role of AI in achieving the Sustainable Development Goals (SDGs) AI for a prosperous 21st century Transparency, automated decision-making processes, and personal profiling and Measuring the relevance of Digitalization and Artificial Intelligence (D&AI) at the indicator level of SDGs. The research-backed panel discussion was dedicated to recognize and prioritize the agenda for addressing the pressing research gaps for academic research, funding bodies, professionals, as well as industry with an emphasis on the transportation sector. A common conclusion across these themes was the need to go beyond the development of AI in sectorial silos, so as to understand the impacts AI might have across societal, environmental, and economic outcomes. The recordings of the panel discussion can be found at: https://www.kth.se/en/2.18487/evenemang/the-role-of-ai-in-achieving-the-sdgs-enabler-or-inhibitor-1.1001364?date=2020â 08â 20&length=1&orglength=185&orgdate=2020â 06â 30 Short link: https://bit.ly/2Kap1tE © 2021"The authors acknowledge the KTH Sustainability Office and the KTH Digitalization Platform for their provided funding, which enabled the organization of this panel discussion. SG acknowledges the funding provided by the German Federal Ministry for Education and Research (BMBF) for the project “digitainable”. SDL acknowledges support through the Spanish Governmen

Home monitoring of physiology and symptoms to detect interstitial lung disease exacerbations and progression:a systematic review

Background: Acute exacerbations (AEs) and disease progression in interstitial lung disease (ILD) pose important challenges to clinicians and patients. AEs of ILD are variable in presentation but may result in rapid progression of ILD, respiratory failure and death. However, in many cases AEs of ILD may go unrecognised so that their true impact and response to therapy is unknown. The potential for home monitoring to facilitate early, and accurate, identification of AE and/or ILD progression has gained interest. With increasing evidence available, there is a need for a systematic review on home monitoring of patients with ILD to summarise the existing data. The aim of this review was to systematically evaluate the evidence for use of home monitoring for early detection of exacerbations and/or progression of ILD. Method: We searched Ovid-EMBASE, MEDLINE and CINAHL using Medical Subject Headings (MeSH) terms in accordance with the PRISMA guidelines (PROSPERO registration number CRD42020215166). Results: 13 studies involving 968 patients have demonstrated that home monitoring is feasible and of potential benefit in patients with ILD. Nine studies reported that mean adherence to home monitoring was &gt;75%, and where spirometry was performed there was a significant correlation (r=0.72–0.98, p&lt;0.001) between home and hospital-based readings. Two studies suggested that home monitoring of forced vital capacity might facilitate detection of progression in idiopathic pulmonary fibrosis. Conclusion: Despite the fact that individual studies in this systematic review provide supportive evidence suggesting the feasibility and utility of home monitoring in ILD, further studies are necessary to quantify the potential of home monitoring to detect disease progression and/or AEs.</p

Machine learning in critical care: state-of-the-art and a sepsis case study

Background: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. Results: The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. Conclusion: We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.Peer ReviewedPostprint (published version