scholarly journals How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Life ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 488
Author(s):  
Kamil Zeleňák ◽  
Antonín Krajina ◽  
Lukas Meyer ◽  
Jens Fiehler ◽  
Daniel Behme ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Action Plan ◽  
Stroke Care ◽  
Limiting Factor ◽  
Ethical Problems ◽  
Innovative Technologies ◽  
Care Processes ◽  
Prehospital Diagnosis ◽  
New Treatment ◽  
Internet Networks

Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

scholarly journals El sesgo de la máquina en la toma de decisiones en el proceso penal.

2020 ◽  
Vol 6 (2) ◽  
pp. 54-71
Author(s):  
Raquel Borges Blázquez
Keyword(s):  
Artificial Intelligence ◽  
The Other ◽  
Human Being ◽  
Criminal Process ◽  
Ethical Problems ◽  
Intelligent Machines ◽  
Other Hand ◽  
Toma De Decisiones ◽  
Legal Thinking ◽  
The One

Artificial intelligence has countless advantages in our lives. On the one hand, computer’s capacity to store and connect data is far superior to human capacity. On the other hand, its “intelligence” also involves deep ethical problems that the law must respond to. I say “intelligence” because nowadays machines are not intelligent. Machines only use the data that a human being has previously offered as true. The truth is relative and the data will have the same biases and prejudices as the human who programs the machine. In other words, machines will be racist, sexist and classist if their programmers are. Furthermore, we are facing a new problem: the difficulty to understand the algorithm of those who apply the law.This situation forces us to rethink the criminal process, including artificial intelligence and spinning very thinly indicating how, when, why and under what assumptions we can make use of artificial intelligence and, above all, who is going to program it. At the end of the day, as Silvia Barona indicates, perhaps the question should be: who is going to control global legal thinking?

scholarly journals Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence

Stroke ◽  
2021 ◽  
Author(s):  
Raul G. Nogueira ◽  
Jason M. Davies ◽  
Rishi Gupta ◽  
Ameer E. Hassan ◽  
Thomas Devlin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Acute Stroke ◽  
Systems Of Care ◽  
Ct Perfusion ◽  
Stroke Care ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion ◽  
Number Of Patients ◽  
The Impact

Background and Purpose: The degree to which the coronavirus disease 2019 (COVID-19) pandemic has affected systems of care, in particular, those for time-sensitive conditions such as stroke, remains poorly quantified. We sought to evaluate the impact of COVID-19 in the overall screening for acute stroke utilizing a commercial clinical artificial intelligence platform. Methods: Data were derived from the Viz Platform, an artificial intelligence application designed to optimize the workflow of patients with acute stroke. Neuroimaging data on suspected patients with stroke across 97 hospitals in 20 US states were collected in real time and retrospectively analyzed with the number of patients undergoing imaging screening serving as a surrogate for the amount of stroke care. The main outcome measures were the number of computed tomography (CT) angiography, CT perfusion, large vessel occlusions (defined according to the automated software detection), and severe strokes on CT perfusion (defined as those with hypoperfusion volumes >70 mL) normalized as number of patients per day per hospital. Data from the prepandemic (November 4, 2019 to February 29, 2020) and pandemic (March 1 to May 10, 2020) periods were compared at national and state levels. Correlations were made between the inter-period changes in imaging screening, stroke hospitalizations, and thrombectomy procedures using state-specific sampling. Results: A total of 23 223 patients were included. The incidence of large vessel occlusion on CT angiography and severe strokes on CT perfusion were 11.2% (n=2602) and 14.7% (n=1229/8328), respectively. There were significant declines in the overall number of CT angiographies (−22.8%; 1.39–1.07 patients/day per hospital, P <0.001) and CT perfusion (−26.1%; 0.50–0.37 patients/day per hospital, P <0.001) as well as in the incidence of large vessel occlusion (−17.1%; 0.15–0.13 patients/day per hospital, P <0.001) and severe strokes on CT perfusion (−16.7%; 0.12–0.10 patients/day per hospital, P <0.005). The sampled cohort showed similar declines in the rates of large vessel occlusions versus thrombectomy (18.8% versus 19.5%, P =0.9) and comprehensive stroke center hospitalizations (18.8% versus 11.0%, P =0.4). Conclusions: A significant decline in stroke imaging screening has occurred during the COVID-19 pandemic. This analysis underscores the broader application of artificial intelligence neuroimaging platforms for the real-time monitoring of stroke systems of care.

scholarly journals Ethical issues in computational pathology

2021 ◽  
pp. medethics-2020-107024
Author(s):  
Tom Sorell ◽  
Nasir Rajpoot ◽  
Clare Verrill
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Ethical Issues ◽  
Recent Literature ◽  
Personal Data ◽  
Ethical Problems ◽  
Data Ethics ◽  
Slide Image ◽  
Data Protection Law ◽  
Computational Pathology

This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from (1) the tension between artificial intelligence (AI) research—with its hunger for more and more data—and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; (2) the fact that computational pathology lends itself to kinds of data fusion that go against data ethics norms and some norms of biobanking; (3) the fact that AI methods are esoteric and produce results that are sometimes unexplainable (the so-called ‘black box’problem) and (4) the fact that computational pathology is particularly dependent on scanning technology manufacturers with interests of their own in profit-making from data collection. We shall suggest that most of these issues are resolvable.

Establishing Stroke Services in the Republic of Georgia

2021 ◽  
pp. 1-9
Author(s):  
Zaza Katsarava ◽  
Tamar Akhvlediani ◽  
Tamar Janelidze ◽  
Tamar Gudadze ◽  
Marina Todua ◽  
...  
Keyword(s):  
Treatment Process ◽  
Stroke Care ◽  
Subsequent Treatment ◽  
National Guidelines ◽  
Ethical Problems ◽  
Stroke Units ◽  
Systemic Thrombolysis ◽  
Stroke Services ◽  
The Republic ◽  
Quality Reports

<b><i>Introduction:</i></b> This article summarizes the medical experience in establishing stroke units and systemic thrombolysis in Georgia, which, like many other post-Soviet countries, still faces problems in organizing stroke care even after 30 years of independence. <b><i>Patients and Methods:</i></b> We created an example of treating acute stroke with systemic thrombolysis and introduced stroke units in several hospitals in the country, including standardization of the diagnostic and treatment process, consistent evaluation, and monthly feedback to the stroke unit staff. <b><i>Results:</i></b> Systemic thrombolysis has become a clinical routine in some large hospitals and is meanwhile reimbursed by the state insurance. The data of consecutive 1,707 stroke patients in 4 major cities demonstrated significant time lost at the prehospital level, due to failure in identifying stroke symptoms, delay in notification, or transportation. The consequent quality reports resulted in a dramatic increase in adherence to the European and national guidelines. A mandatory dysphagia screening and subsequent treatment led to a decrease in pneumonia rates. <b><i>Discussion:</i></b> We discuss our experience and suggestions on how to overcome clinical, financial, and ethical problems in establishing a stroke services in a developing country. <b><i>Conclusion:</i></b> The Georgian example might be useful for doctors in other post-Soviet countries or other parts of the world.

Abstract 18: Does Quality Improvement Training Add to Audit and Feedback for Inpatient Stroke Care Processes?

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Linda S Williams ◽  
Virginia Daggett ◽  
James Slaven ◽  
Zhangsheng Yu ◽  
Danielle Sager ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Data Collection ◽  
Group Interaction ◽  
Logistic Models ◽  
Stroke Care ◽  
Audit And Feedback ◽  
Dvt Prophylaxis ◽  
Care Processes ◽  
Data Collection Program ◽  
Collection Program

Background: Despite advances in stroke care, many patients do not receive recommended care processes. Quality indicator (QI) reporting programs, like GWTG-Stroke, have been shown to improve care. We sought to determine whether training plus QI feedback was more effective than QI feedback alone in improving two stroke QIs. Methods: We conducted a cluster randomized trial in 11 VA hospitals. Sites were randomized to a quality improvement training program plus QI feedback vs. QI feedback alone to improve DVT prophylaxis and dysphagia screening. Intervention sites received face-to-face training, developed individualized improvement plans, and had 6 months of post-training facilitation. Both groups received monthly QI feedback. Eligibility and passing for the two stroke QIs, plus nine other stroke QIs, was determined by centralized chart review. We compared pre-intervention (pre-I) to post-intervention (post-I) performance on the two stroke QIs and on defect-free care (DF, a binary patient-level variable including all QIs) in intervention vs. control sites. We constructed logistic models of the two QIs and DF care, adjusting for patient variables, time, intervention group, and time-group interaction. Results: The five intervention sites had 1147 admissions and the six control sites had 1017 admissions during the study period. DVT prophylaxis was similar pre-I (85% vs. 90%) and improved in both groups (post-I rates 90% intervention and 94% control, ratio of ORs 0.89, p = 0.75). Dysphagia screening was higher pre-I in intervention sites (51% vs. 37%), and improved more in the control sites (post-I 56% and 52%, ratio of ORs 0.67, p=0.04). In logistic models, DVT, Dysphagia, and DF performance were associated with baseline performance and post-I time. Dysphagia performance was also associated with NIHSS and time-group interaction, and DF care was also associated with the presence of a baseline data collection program. Conclusion: Quality improvement training did not add to the impact of data feedback in sites already motivated to participate in QI initiatives. Defect-free stroke care is associated with an ongoing stroke data collection program, emphasizing the importance of audit and feedback to achieve the highest quality stroke care.

7. Into the 21st century

2018 ◽  
pp. 112-124 ◽  
Author(s):  
Tom Burns
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Virtual Reality ◽  
21St Century ◽  
Mental Illnesses ◽  
New Science ◽  
The Core ◽  
New Treatment ◽  
The Brain ◽  
Manage Mental

‘Into the 21st century’ explains how there is an increased focus on how our body, and not just the brain, influences our mental health. Rapidly advancing computer technology, including artificial intelligence and virtual reality, is beginning to provide new treatment possibilities, not just support and simplify the old ones. The development of sophisticated imaging has supercharged the area of neurosciences and the increased understanding of genetics and the new science of epigenetics provide psychiatry with greater tools to identify and manage mental illnesses. A paradox with our increasingly technological and scientific advances is that the core dilemmas of psychiatry appear not to be diminishing. Psychiatry will survive the 21st century, but certainly it is changing.

A Comprehensive Study on Artificial Intelligence and Robotics for Machine Intelligence

2021 ◽  
pp. 203-222
Author(s):  
Nagadevi Darapureddy ◽  
Muralidhar Kurni ◽  
Saritha K.
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Machine Intelligence ◽  
Ethical Problems ◽  
Economic Impact Assessment ◽  
The Social ◽  
Learning Capabilities ◽  
Increase Productivity ◽  
The Impact ◽  
And Robotics

Artificial intelligence (AI) refers to science-generating devices with functions like reasoning, thinking, learning, and planning. A robot is an intelligent artificial machine capable of sensing and interacting with its environment utilizing integrated sensors or computer vision. In the present day, AI has become a more familiar presence in robotic resolutions, introducing flexibility and learning capabilities. A robot with AI provides new opportunities for industries to produce work safer, save valuable time, and increase productivity. Economic impact assessment and awareness of the social, legal, and ethical problems of robotics and AI are essential to optimize the advantages of these innovations while minimizing adverse effects. The impact of AI and robots affects healthcare, manufacturing, transport, and jobs in logistics, security, retail, agri-food, and construction. The chapter outlines the vision of AI, robot's timeline, highlighting robot's limitations, hence embedding AI to robotic real-world applications to get an optimized solution.

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