Benefits Of Artificial Intelligence Research Articles

Research Paper: Artificial Intelligence in Medical: JEEV AI

Artificial intelligence is one of the most discussed topics of the present time.The burning question of today about artificial intelligence is “will it be beneficial or dangerous for a human being”. This MODEL analyzes the benefits of artificial intelligence in medicine. It examines how artificial intelligence assists the medical field as well as how patient's health is affected using this popular phenomenon in diagnosing diseases, patient's treatment, reducing errors, and virtually being present with the patients.In this model we have the data of previous patients and have created a machine learning model that tells what is the probability of a person suffering from that skin disease and after running the machine learning algorithm we have make an model as Dermatologists examine skin lesions by visual inspection and dermoscopy, similarly we have used our JEEV AI device to do skin lesion examination based on AI algorithms and machine vision. So we have created a SMS based chat bot through which user can send his feedback on his skin health conditions to our best crowd sourced doctors, Three-Stage Healthcare support system in covid time so people can get their skin problems solved at home by getting best treatment in covid time

Challenges and transformative opportunities in superconductor vortex physics

In superconductors, the motion of vortices introduces unwanted dissipation that is disruptive to applications. Fortunately, material defects can immobilize vortices, acting as vortex pinning centers, which engenders dramatic improvements in superconductor material properties and device operation. This has motivated decades of research into developing methods of tailoring the disorder landscape in superconductors to increase the strength of vortex pinning. Yet, efficacious materials engineering still eludes us. The electromagnetic properties of real (disordered) superconducting materials cannot yet be reliably predicted, such that designing superconductors for applications remains a largely inefficient process of trial and error. This is ultimately due to large gaps in our knowledge of vortex dynamics: the field is challenged by the extremely complex interplay between vortex elasticity, vortex–vortex interactions, and material disorder. In this Perspective, we review obstacles and recent successes in understanding and controlling vortex dynamics in superconducting materials and devices. We further identify major open questions and discuss opportunities for transformative research in the field. This includes improving our understanding of vortex creep, determining and reaching the ceiling for the critical current, advanced microscopy to garner accurate structure–property relationships, frontiers in predictive simulations and the benefits of artificial intelligence, as well as controlling and exploiting vortices in quantum information applications.

Big Data and Artificial Intelligence for Financial Inclusion: Benefits and Issues

This paper discusses the benefits and issues associated with big data and artificial intelligence (AI) for financial inclusion. The benefits of artificial intelligence and big data for financial inclusion are: improved efficiency and risk management for financial services providers; the provision of smart financial products and services to banked adults; simplification of the account opening process for unbanked adults and the creation of credit scores for unbanked adults using alternative information. Several issues associated with artificial intelligence and big data for financial inclusion that need to be addressed include: the shortage of skilled AI workers, increase in the level of unemployment in the financial ecosystem, the unconscious bias in the design of artificial intelligence systems, and other barriers caused by strict data privacy laws.

ARTIFICIAL INTELLIGENCE PRACTICES IN THE HEALTH SECTOR

Artificial intelligence is a technology developed to make machines think like humans. Aristotle's historical artificial intelligence entered the health sector in the 1970s. The first application for artificial intelligence in the internist-1 field in health care, the Casnet expert system, and MYCIN. This technology was later introduced to many areas of health care. The main purpose of this application is for the benefit of doctors and patients. In general, applications, medical decisions, early diagnosis and treatment, drug development, and medical imaging issues deserve attention. Another important issue is the concern about the benefits of artificial intelligence, as well as the possibility of replacing the physicians in charge when medical decisions are left entirely to artificial intelligence. This study aims to provide readers with general information about the areas where artificial intelligence is used in health care. In this study, 14 applications were examined and outstanding results were observed. When doctors say that a comatose patient cannot wake up, artificial intelligence says that a comatose patient can wake up and the patient can wake up. Other advanced artificial intelligence has shown that it can detect cancer more accurately than 58 skin specialists. One study concluded that artificial intelligence with a predictive treatment approach lowered health care costs by 5 to 9% and could lead to an increase in life expectancy of 0.2 to 1.3 years. Keywords: Artificial Intelligence, Health Practices, Health Management

التحدیات القانونیة الدولیة لتنظیم الذکاء الاصطناعی: حالة الأسلحة الآلیة ذاتیة التشغیل

شهد المجتمع الدولي تحولا جذريا نتيجة للثورة الصناعية الرابعة والثورة الرقمية، إلا أنه في الوقت ذاته بدأ يعاني من عدد من التحديات والمخاطر الأخلاقية والقانونية. ويقود الذکاء الاصطناعي ثورة عسکرية تکنولوجية تتميز بعدد متزايد من المهام التي يمکن القيام بها والتي تفوق بکثير القدرات البشرية. ومن المؤکد أن التغير في شکل الحروب نتيجة للتکنولوجيا المتطورة سوف يغير في مفهوم وتطبيق القواعد الدولية. أن المکاسب الکبيرة للذکاء الاصطناعي يجب أن يتم مراقبتها من قبل النظام العالمي لتنظيم استغلاله والعمل على التأکد من وجود نظام يحترم القيم والأخلاق بالشکل الذي يتوافق مع المبادئ والقواعد الدولية وبالأخص القانون الدولي الإنساني والقانون الدولي لحقوق الانسان. وتتطلب هذه التحديات التعاون والتنسيق الکامل مع کافة الفواعل الموجودة في المجتمع الدولي وعلى جميع المستويات. The international society was profoundly evolved as a result of the fourth industrial revolution “Digital revolution”, but it raises at the same time major ethical and legal risks and challenges. The artificial intelligence leads a military technological revolution marked by a growing number of tasks that could be performed and which exceeds the capability of humans. The transformation of the character of wars due to the new innovative technologies will inevitably change the perception and the application of international rules. The benefits of Artificial intelligence should be supervised in a global system that regulates its exploitation and ensure that a system of ethics and values are respected in a way that complies with the principles and regulations in international society and especially, the International Humanitarian Law and International Human rights Law. These challenges require collaboration and cooperation with all the actors of international society and at all the levels.

Keeping the "Human in the Loop" in the Age of Artificial Intelligence : Accompanying Commentary for "Correcting the Brain?" by Rainey and Erden.

The benefits of Artificial Intelligence (AI) in medicine are unquestionable and it is unlikely that the pace of its development will slow down. From better diagnosis, prognosis, and prevention to more precise surgical procedures, AI has the potential to offer unique opportunities to enhance patient care and improve clinical practice overall. However, at this stage of AI technology development it is unclear whether it will de-humanize or re-humanize medicine. Will AI allow clinicians to spend less time on administrative tasks and technology related procedures and more time being present in person to attend to the needs of their patients? Or will AI dramatically increase the presence of smart technology in the clinical context to a point of undermining the humane dimension of the patient-physician relationship? In this brief commentary, we argue that technological solutions should be only integrated into clinical medicine if they fulfill the following three conditions: (1) they serve human ends; (2) they respect personal identity; and (3) they promote human interaction. These three conditions form the moral imperative of humanity.

The use of patient health information outside the circle of care: Consent preferences of patients from a large academic cancer centre.

e14122 Background: Massive volumes of patient health information (PHI) are required to realize the anticipated benefits of artificial intelligence in future clinical medicine. To maintain public trust in medical research however, consent policies must evolve to reflect contemporary patient preferences. Methods: From January-December 2019, patients attending clinics at a large academic cancer centre were invited to complete a 27-item iPad survey on consent preferences. Survey items focused on: (a) broad vs. specific consent; (b) opt-in vs. opt-out approaches for research contact; (c) comfort sharing with different recipients; (d) perceptions on commercialization; and (e) options to track information use and study results. Demographic questions addressed cancer type, treatment stage, age, gender, ethnicity, education level, and household income. Results: A total of 222 participants were included in the analysis (112 males, 108 females; 2 rather not say); 83% were comfortable sharing PHI with researchers at their own hospital. While 56% of patients preferred broad consent, 38% preferred to be contacted with study details and asked to consent every time (specific consent); 6% prefer not to share at all. Younger patients ( < 49 years) most often chose specific consent (50%); significantly more than those 75+ years (24%; p < .05). Younger patients ( < 49 years) were also significantly more uncomfortable than older patients (50+ years) sharing even within their own hospital (13% uncomfortable vs. 1% uncomfortable; p < .05). A significant majority of patients (63%, p = .0001) preferred to be asked for permission before being entered into a contact pool vs. automatic entry with opportunity to opt-out. The majority of patients were uncomfortable sharing PHI with commercial enterprises (51% uncomfortable, 27% comfortable, 22% neutral). A significant majority expressed the desire to track who is using their PHI (61%, p < .0001), and be notified regarding study results (70%, p < .0001). Conclusions: While most patients were willing to share their PHI with researchers at their own hospital, many preferred a transparent and reciprocal consent process. These data also suggest a generational shift, wherein younger patients preferred more informed consent options. Modernizing consent policies to reflect increased patient interest in the exercise of their autonomy is crucial in fostering sustained public engagement in medical research.

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPRESEARCH).

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat diabetes and its complications. Applying artificial intelligence in complimenting the diagnosis, management, and prediction of the diabetes trajectory has been increasingly common over the years. This study aims to illustrate an inclusive landscape of application of artificial intelligence in diabetes through a bibliographic analysis and offers future direction for research. Bibliometrics analysis was combined with exploratory factor analysis and latent Dirichlet allocation to uncover emergent research domains and topics related to artificial intelligence and diabetes. Data were extracted from the Web of Science Core Collection database. The results showed a rising trend in the number of papers and citations concerning AI applications in diabetes, especially since 2010. The nucleus driving the research and development of AI in diabetes is centered around developed countries, mainly consisting of the United States, which contributed 44.1% of the publications. Our analyses uncovered the top five emerging research domains to be: (i) use of artificial intelligence in diagnosis of diabetes, (ii) risk assessment of diabetes and its complications, (iii) role of artificial intelligence in novel treatments and monitoring in diabetes, (iv) application of telehealth and wearable technology in the daily management of diabetes, and (v) robotic surgical outcomes with diabetes as a comorbid. Despite the benefits of artificial intelligence, challenges with system accuracy, validity, and confidentiality breach will need to be tackled before being widely applied for patients’ benefits.

ETHICAL AND LEGAL ASPECTS OF THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTH AND NURSING CARE

The aim of this paper is to analyse current approaches and perspectives on the development of ethical and legal regulation of artificial intelligence with special regard to its use in health and nursing care. Inextricably linked to this is an analysis of current international and national binding legislation, as well as legally non-binding instruments designed for artificial intelligence developers, manufacturers and users. In addition to the positive benefits of artificial intelligence in health and nursing care, we point out the ethical and legal aspects of its use, such as the legal personality of artificial intelligence, patients' right to privacy, personal data protection and civil liability for damage caused by artificial intelligence. We place the main emphasis on the legislation of the European Union, which has a direct impact on the national legislation of the Slovak Republic.

Business Process Reengineering: A Scope of Automation in Software Project Management using Artificial Intelligence

This research paper aims an analytical study on the software development organization insight into trending automation technologies and their implementation Software Engineering Management (SEM) processes. Software Project Management (SPM) is a scientific art for planning, controlling execution and monitoring. SPM approaches are more focusing towards the essential requirement for the success of software project development. It has been very challenging to manage software development using existing project management procedures driven by software development organizations and this is one of the areas of problem statement for this research. This paper discusses an analytical study for the requirements and consideration of BPR in SPM, explores to spot and emphasizes the important success factors for the execution of a BPR using benefits of Artificial Intelligence (AI) in software development organization. BPR is organizational mechanism that improves ability to respond to challenges of qualitative result by change and improvement in software engineering processes, productivity, product quality and competitive advantages. AI will be the best approach and scope of automation SEM processes for software development organizations. This paper also represents a conceptual view of software engineering model shift for improvements in capability of project managers to handle agile thinking and problem solving for betterment of SPM using Artificial Intelligence.

Design Thinking for a User-Centered Approach to Artificial Intelligence

This is a commentary on Michael Lissack’s two-part article “Understanding is a Design Problem: Cognizing from a Designerly Thinking Perspective.” Artificial intelligence benefits us by enabling safer cars, more efficient homes, smarter investments, and personalized recommendations on everything from news to clothing. Machines do not act on their own. Our consumption and production decisions, whether active or passive, influence the algorithms that have become the touchstone of modern life. As such, we bear the responsibility of exercising our agency to think critically about the information served to us. User research can help by ensuring that new designs are both usable and useful and, throughout this process, the design thinking framework helps us take a step back and remain open minded, consider alternative points of view, watch for bias, recognize adjacent possibilities, and innovate. By engaging in this uniquely human thinking, we remain engaged, relevant, and active participants in shaping our present and future.

Application of Multi-Agent Technology in Decision Supporting

In the study, the development course and present situation of decision support technology is analyzed, and now decision support technology is lack and it is need to solved, it should combine the benefits of artificial intelligence as in the people's way of thinking to solve practical problems. In addition it also needs to have the ability to identify problems, and skills of dealing with distributed collaboration. The Multi-Agent technology in decision support research is introduced, the decision support is based on the Multi-Agent technology, and in order to demonstrate it can meet the specific needs of today's decision support, it analyzes the process of its implementation applying agent modeling theory, the behavior of the agent coordination mechanism and communication mechanism between the agents and so on, and puts forward the corresponding implementation scheme. Finally the effectiveness of the proposed technology and feasibility are analyzed, related results show that it can be applied in the actual decision support. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3970

Patient behavior and the benefits of artificial intelligence: The perils of “dangerous” literacy and illusory patient empowerment

ObjectiveArtificial intelligence can provide important support of patient health. However, limits to realized benefits can arise as patients assume an active role in their health decisions. MethodsDistinguishing the concepts of health literacy and patient empowerment, we analyze conditions that bias patient use of the Internet and limit access to and impact of artificial intelligence. ResultsImproving health literacy in the face of the Internet requires significant guidance. Patients must be directed toward the appropriate tools and also provided with key background knowledge enabling them to use the tools and capitalize on the artificial intelligence technology. ConclusionBenefits of tools employing artificial intelligence to promote health cannot be realized without recognizing and addressing the patients’ desires, expectations, and limitations that impact their Internet behavior. In order to benefit from artificial intelligence, patients need a substantial level of background knowledge and skill in information use—i.e., health literacy. Practice implicationsIt is critical that health professionals respond to patient search for information on the Internet, first by guiding their search to relevant, authoritative, and responsive sources, and second by educating patients about how to interpret the information they are likely to encounter.

Efficacious End User Measures—Part 1: Relative Class Size and End User Problem Domains

Biological and medical endeavors are beginning to realize the benefits of artificial intelligence and machine learning. However, classification, prediction, and diagnostic (CPD) errors can cause significant losses, even loss of life. Hence, end users are best served when they have performance information relevant to their needs, this paper’s focus. Relative class size (rCS) is commonly recognized as a confounding factor in CPD evaluation. Unfortunately, rCS-invariant measures are not easily mapped to end user conditions. We determine a cause of rCS invariance, joint probability table (JPT) normalization. JPT normalization means that more end user efficacious measures can be used without sacrificing invariance. An important revelation is that without data normalization, the Matthews correlation coefficient (MCC) and information coefficient (IC) are not relative class size invariants; this is a potential source of confusion, as we found not all reports using MCC or IC normalize their data. We derive MCC rCS-invariant expression. JPT normalization can be extended to allow JPT rCS to be set to any desired value (JPT tuning). This makes sensitivity analysis feasible, a benefit to both applied researchers and practitioners (end users). We apply our findings to two published CPD studies to illustrate how end users benefit.

Four methods for short-term load forecasting using the benefits of artificial intelligence

Four methods are developed for short-term load forecasting and are tested with the actual data from the Turkish Electrical Authority. The method giving the most successful forecasts is a hybrid neural network model which combines off-line and on-line learning and performs real-time forecasts 24-hours in advance. Loads from all day types are predicted with 1.7273% average error for working days, 1.7506% for Saturdays and 2.0605% for Sundays.