Intelligence

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. Key points: 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 [1]. For sure that we have already heard about how mathematics can improve the anticancer arsenal [2]. 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 [1].

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.

Saliva is produced by and secreted from salivary glands. It is an extra-cellular fluid, 98% water, plus electrolytes, mucus, white blood cells, epithelial cells, enzymes, and anti-microbial agents. Saliva serves a critical role in the maintenance of oral, dental, and general health and well-being. Hence, alteration(s) in the amount/quantity and/or quality of secreted saliva may induce the development of several oro-dental variations, thereby the negatively-impacting overall quality of life. Diverse factors may affect the process of saliva production and quantity/quality of secretion, including medications, systemic or local pathologies and/or reversible/irreversible damage. Herein, chemo- and/or radio-therapy, particularly, in cases of head and neck cancer, for example, are well-documented to induce serious damage and dysfunction to the radio-sensitive salivary gland tissue, resulting in hypo-salivation, xerostomia (dry mouth) as well as numerous other adverse Intra-/extra-oral, medical and quality-of-life issues. Indeed, radio-therapy inevitably causes damage to the normal head and neck tissues including nerve structures (brain stem, spinal cord, and brachial plexus), mucous membranes, and swallowing muscles. Current commercially-available remedies as well as therapeutic interventions provide only temporary symptom relief, hence, do not address irreversible glandular damage. Further, despite salivary gland-sparing techniques and modified dosing strategies, long-term hypo-function remains a significant problem. Although a single governing mechanism of radiation-induced salivary gland tissue damage and dysfunction has not been yet elucidated, the potential for synergy in radio-protection (mainly, and possibly -reparation) via a combinatorial approach of mechanistically distinct strategies, has been suggested and explored over the years. This is, undoubtfully, in parallel to the ongoing efforts in improving the precision, safety, delivery, and efficacy of clinical radiotherapy protocols/outcomes, and in designing, developing, evaluating and optimizing (for translation) new artificial intelligence, technological and bio-pharmaceutical alternatives, topics covered in this review.

The creation of the information society is associated with the creation of new intellectual, cultural, spiritual and material values, as well as with new principles and methods of social and interpersonal communication. Achieving this goal is impossible without changes in teaching methodology, teaching technologies and teacher’s work.The article is an overview and focuses on the following issues. In the information society, the era of biocomputers and quantum computers is coming, which will use not only artificial intelligence, but also artificial consciousness for simulation. Artificial awareness builds the foundations for the development of robots that will be widely used in various fields of industry and science. - Artificial awareness combined with artificial intelligence can be an innovative method in education and communication; - Quantum computers and biocomputers will find wide application in human education and social life;

This article reviews the latest advancements in orthopaedic interventions, including robotic-assisted surgery, 3D printing, nanotechnology, and biological approaches. The potential impact of phage therapy on preventing infections caused by antibiotic-resistant bacteria in orthopaedic surgery patients is also discussed. Additionally, the article explores future perspectives for orthopaedic interventions, including personalized medicine, artificial intelligence, and regenerative medicine. As the field of orthopaedics continues to evolve, these advancements have the potential to significantly improve patient outcomes and revolutionize the field.

Background: Hemp seed (Cannabis sativa L.) is an annual herbaceous plant of the Cannabis genus that contains a large amount of protein, iron, and fatty acids, including linoleic, α-linolenic, and γ-linolenic acid. These compounds are involved in a number of biological activities, including immunity enhancement, hyperlipidemia, and inflammation reduction. Here, we investigated the antioxidant effects of hemp seed on human cognitive function.Methods: The test was administered to 34 healthy volunteers aged ≥ 20 years. Participants were selected according to age and sex and were administered 10 g of hemp seed three times daily (30 g/day) for 45 days. The outcome measurements were recorded using a survey, computerized neurocognitive tests, and artificial intelligence.Results: Survey analysis determined that both the Beck Anxiety Inventory and Beck Depression Inventory measurements decreased significantly after hemp seed consumption when compared to measurements taken before consumption (p < 0.05). Additionally, significant results were observed in the Stroop and Tower of London tasks (p < 0.05). The prediction performance for the antidepressant effect was 0.83 for the area under the curve in the random forest algorithm, which was superior to that of other machine learning methods. Conclusion: These results suggest that hemp seeds have a beneficial effect on cognitive impairment.

Dentistry is an ever-evolving field that has seen significant advances in recent years. This article sheds light on some of the current and emerging trends in oral health care, including digital dentistry, regenerative medicine, and the use of lasers. For example, digital dentistry involves the use of computer-aided design and manufacturing technology, which enables more accurate and efficient production of dental devices. On the other hand, regenerative medicine and nanoDentistry can be considered promising area that combines the use of stem cells, growth factors, biomaterials, and nanotechnology to regenerate damaged tissue and improve treatment outcomes. Lasers are increasingly being used in dentistry for a range of applications, including the treatment of gum disease and teeth whitening. Other developing technologies such as 3D printing and artificial intelligence are also being increasingly incorporated into dentistry, providing improved treatment options for our patients. Last yet definitely would/will not least, controlled drug delivery systems are being developed to deliver drugs to specific target sites in a localized and sustained manner, reducing the risk of adverse effects. Currently, these emerging trends are transforming the landscape of odontology and beyond. Hence, in this mini-Review, we explore such trends in oro-dental and cranio-maxillo-facial indications to highlight the potential benefits, advancements, and opportunities of applications for improved patient care.

Coronavirus disease (COVID-19) is a viral pneumonia that is found in China and has spread globally. Early diagnosis is important for effective and timely treatment. Thus, many ongoing studies attempt to solve key COVID-19 problems such as workload classification, detection, and differentiation from other pneumonia and healthy lungs using different imaging modalities. Researchers have identified some limitations in the deployment of deep learning methods to detect COVID-19, but there are still unmet challenges to be addressed. The use of binary classifiers or building classifiers based on only a few classes is some of the limitations that most of the existing research on the COVID-19 classification problem suffers from. Additionally, most prior studies have focused on model or ensemble models that depend on a flat single-feature imaging modality without using any clinical information or benefiting from the hierarchical structure of pneumonia, which leads to clinical challenges, and evaluated their systems using a small public dataset. Additionally, reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays. Radiologists, computer scientists, and physicians all need to come to an understanding of these interdisciplinary issues. This article first highlights the challenges of deep learning deployment for COVID-19 detection using a literature review and document analysis. Second, it provides six key recommendations that could assist future researchers in this field in improving the diagnostic process for COVID-19. However, there is a need for a collective effort from all of them to consider the provided recommendations to effectively solve these issues.