Clinical applications of Artificial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis influencing general healthcare have not materialized, with few exceptions, and then predominantly in the field of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient’s care, as well as streamline workflow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people.
1. Artificial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care.
2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations.
3. Telemedicine and robot assisted technology are well received by older service users.
4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting.
Modern-day enhancements in Enterprise Architectures (EA) has increased the interoperability issues in almost all domains; these issues are increasing day-by-day as organizations are spanning and information is being exchanged between different platforms. Command Control Computer Communication and Intelligence (C4I) complex systems are also facing the interoperability issues due to highly classified and sensitive information being exchanged. In this paper we have discussed the integration of different C4I applications running under heterogeneous platforms by allowing them to communicate using a secure and ciphered web based middleware named as Web Middleware (WMW). This middleware is a client-server based web adaptor to achieve clean, systematic, secure and reliable communication. The main feature among many is the simple HTTP browser based customization that do not require any specific or special add-ons and controls to be installed on the client machine. Architecture usage, and initialization of the WMW middleware is discussed with security and performance discussion.
Nobody doubts that mathematics plays a crucial role in medical achievements. It is certain that is being mainly used in statistics and physics for biomedical problems . For sure that we have already heard about how mathematics can improve the anticancer arsenal . Quantitative genetics have triggered a giant potential in medical care [3,4]. And mathematical algorithms, provided by artificial intelligence, continuously boost new therapeutic paradigms [5,6]. Nonetheless, one cannot ignore the ability of mathematics for analyzing ideas.
The concept of space-matter motion in the new Cartesian physics, based on the identity of space and matter, creates the basis for the study of consciousness as the action of the brain in space inside and outside itself and offers a way of materialistic explanation of life on Earth. She claims that consciousness in living matter arises when the brain begins to create the surrounding space the image of themselves and the world. And since space according to Descartes is identical to matter, the images created by the brain of itself and the external world in the surrounding space have a material basis and therefore the displayed organs interact with each other and the external world.
Present piece of idea exhibits to divert attention towards automated high precision Life Support System (LSS) instead of manual one using medical intelligence devices while treating and diagnosis to the patient, where Ventilator, inhaler and respiratory control is most important factor during operation, surgeries and in other likewise medical emergency situations to maintain proper saturation in patient lungs to sustain their lives. This work gives idea, how we can design A.I based Inhaler System for the same.
Artificial intelligence (AI) is the emulation of human intelligence in computers that have been trained to think and behave like humans. The word may also refer to any computer that exhibits human-like characteristics like learning and problem-solving. Artificial intelligence is intelligence demonstrated by machines, as opposed to natural intelligence, which involves consciousness and emotionality and is demonstrated by humans and animals .
To investigate the variables correlation analysis research method for assessing the caregivers’ perceptions in two groups including dependent and independent variables to correlate the measuring of early childhoods. Typically, in correlated data, for jointly normally distributed data with relevant outliers that can use a correlation as a measure of a monotonic association. Designing the 65-paired samples for the Thai Model of early detection and intervention of children as the health care system guidelines from 26-CUPs have compared. Using the DSPM divided into 65-appropriate and 65-inappropriate development early childhoods for every 13 CUPS that depends on talented children. Selecting the Receptive Language (RL) skills identified in contributing growth relative factors with four research instruments: the EPRLS, PRLF, CNRLF, and CMRLF are valid and reliable significantly. Comparisons of the appropriate and inappropriate early childhoods are differences ( < .05), the intercorrelation circumflex nature analysis (p < .05), positively. The R2 values show that 26% and 55% of the variance in training caregivers’ factor skills on the PRLF, CNRLF, and CMRLF to the EPRLS in inappropriate and appropriate early childhoods, respectively. Developmentally Appropriate Practice is a perspective in a child’s development: social, emotional, physical, and cognitive-based on the child’s cultural background: community, family history, and family structure.
The main aim of forensic science is to gather intelligence to enable the judge to credible and logical decisions in the court by means of scientific approach through evaluation of evidence for the administration of justice, and country around the world now considers forensic methodology as the gold standard for criminal investigation. Therefore, the present study examined the level of awareness on the relevance of forensics in criminal investigation in Nigeria. The design used in this study is the survey research design and the sample size of this study was a total of one hundred personnel of law enforcement and the judiciary. The study adopted descriptive statistics which involves the use of frequency and percentage. The result of the present study revealed that the participants were distributed socio-demographically as follows; there was an observable higher number of male participants (68%) relative to the female participants (32%), As per age distribution, a larger population of the participants were found to be > 40 years of age with 55%, and it was observed that age between 35-39 years ranked the least with 15%. On educational level, the result of the present study revealed that majority of the participants possesses a bachelor’s degree as the highest level of educational qualification with 75% from a pool of 100% of participants. The present study further examined responses on the relevance of forensics in criminal investigation, and the result revealed an inadequate level of awareness on the relevance of forensics in criminal investigation. Therefore, the study recommends that the Nigerian Police Force and the Judiciary should collaborate with Universities running programs on forensics for trainings.
Throughout global efforts to defend against the spread of COVID-19 from late 2019 up until now, one of the most crucial factors that has helped combat the pandemic is the development of various screening methods to detect the presence of COVID-19 as conveniently and accurately as possible. One of such methods is the utilization of chest X-Rays (CXRs) to detect anomalies that are concurrent with a patient infected with COVID-19. While yielding results much faster than the traditional RT-PCR test, CXRs tend to be less accurate. Realizing this issue, in our research, we investigated the applications of computer vision in order to better detect COVID-19 from CXRs. Coupled with an extensive image database of CXRs of healthy patients, patients with non-COVID-19 induced pneumonia, and patients positive with COVID-19, convolutional neural networks (CNNs) prove to possess the ability to easily and accurately identify whether or not a patient is infected with COVID-19 in a matter of seconds. Borrowing and adjusting the architectures of three well-tested CNNs: VGG-16, ResNet50, and MobileNetV2, we performed transfer learning and trained three of our own models, then compared and contrasted their differing precisions, accuracies, and efficiencies in correctly labeling patients with and without COVID-19. In the end, all of our models were able to accurately categorize at least 94% of the CXRs, with some performing better than the others; these differences in performance were largely due to the contrasting architectures each of our models borrowed from the three respective CNNs.
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University of Port Harcourt Teaching Hospital, Nigeria
Dr. Elizabeth A Awoyesuku
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BS, PharmD., MBA, CPHIMS, FHIMSS, Adjunct Professor, Global Healthcare Management, MCPHS University, Chief Strategy Offi cer, MedicaSoft, Senior Advisor, National Health IT (NHIT) Collaborative for Underserved, New York HIMSS, National Liaison, Health 2.0 Boston, Past Chair, Chair Innovation, USA
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