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Journal of Hospital Management and Health Policy
AI
This paper explores the synergy between artificial intelligence (AI) and healthcare, emphasizing AI's transformative potential in addressing inefficiencies and challenges within healthcare systems. It examines how AI, particularly through machine learning, enhances diagnosis, prognosis, and therapy while automating administrative tasks and aiding clinical decision-making. The findings suggest that integrating AI can lead to personalized medicine and improved healthcare delivery, ultimately meeting rising demands for quality and cost-effective care.
SSRN Electronic Journal, 2019
To an extent as never before in the history of medicine, computers are supporting human input, decision making and provision of data. In today' s healthcare sector and medical profession, AI, algorithms, robotics and big data are used to derive inferences for monitoring large-scale medical trends, detecting and measuring individual risks and chances based on data-driven estimations. A knowledge-intensive industry like the healthcare profession highly depends on data and analytics to improve therapies and practices. In recent years, there has been tremendous growth in the range of medical information collected, including clinical, genetic, behavioral and environmental data. Every day, healthcare professionals, biomedical researchers and patients produce vast amounts of data from an array of devices. These include electronic health records (EHRs), genome sequencing machines, high-resolution medical imaging, smartphone applications and ubiquitous sensing, as well as Internet of Things (IoT) devices that monitor patient health (OECD 2015). Through machine learning algorithms and unprecedented data storage and computational power, AI technologies have most advanced abilities to gain information, process it *The following article is based on a study initiated and curated by Dr. Dieter Feierabend at NEOS Lab and executed by Julia M. Puaschunder during Summer and Fall of 2019. Funding of the European Liberal Forum at the European Parliament is most gratefully acknowledged.
Studies in Health Technology and Informatics, 2021
The potential value of AI to healthcare, and nursing in particular, ranges from improving quality and efficiency of care to delivering on the promise of personalized and precision medicine. AI systems may become virtually indispensable as ever more data is amassed about every aspect of health. AI can help reduce variability in care, while improving precision, accelerating discovery and reducing disparities. AI can empower patients and potentially allow healthcare professionals to relate to their patients as healers supported by the combined wisdom of the best medical research and analytic technology. There are, however, many challenges to understanding the optimal uses of AI; addressing the technological, systemic, regulatory and attitudinal roadblocks to successful implementation; and integrating AI into the fabric of health care. This paper provides a grounding in the origins and fundamental building blocks of AI, applications in healthcare and for nursing, and the critical challe...
New Trends in Sustainable Business and Consumption
Nowadays, because of the development and the evolving technologies, artificial intelligence (AI) changed the way healthcare systems function and altered the interactions between medical practitioners and patients. In this context, AI has the potential to revolutionize the healthcare system and is an opportunity to strengthen the doctor-patient relationship by offering accurate information in a reasonable amount of time. After a brief journey of the evolution of defining artificial intelligence, the article first covers the meaning of AI in the healthcare system through a systematic literature review, classifying the essential roles of AI in this system. It continues with the explained grade of involvement of the European Union in this field and its decisions during the COVID-19 pandemic regarding the uptake and upscale of digital health solutions. Lastly, this article emphasizes the necessity of having a unitary approach across the European Union regarding the AI involvement in the healthcare system by suggesting the implementation of a single European platform for medical appointments to adapt to the digital world and facilitate medicine without borders. This article aims to provide an overview of the AI roles in the healthcare system that have been developed and implemented and are playing a much-needed role in improving healthcare services, quality of medical practice, and patient satisfaction regarding the services. The classification underlines the positive aspects of adapting the current healthcare system to the new era and embraces the challenges. Artificial intelligence's roles lead to a better approach to the patient's attitude once they know the benefits of using and accepting artificial intelligence technologies in the medical field.
DR. Ram Kumar and DR. Anjna Agarwal, Govt. College of Nursing, GSVM Medical College, Kanpur, (U.P.), & Rajasthan University, Jaipur (Raj.), INDIA, 2020
Artificial intelligence touches nearly every part of our day. The main functions of artificial intelligence areto create expert systems and to implement human intelligence in machines. Artificial intelligence has played a significant role in various fields such as gaming, natural language processing, expert systems, vision systems, speech recognition, hand writing recognition and intelligent robots. Artificial intelligence in healthcare can help cut costs of ongoing health operations and impact the quality of care for clients everywhere.AI can also improve client's outcomes by diagnosing diseases early. Artificial intelligence can be helpful in reducing human errors, increasing productivity, making faster decision-making processes, reducing cost of goods and services, excellent handling of repetitive and frequent tasks, excellent handling of low-level tasks, transformation in healthcare for betterment and improves security. Artificial intelligence can have certain disadvantages also such as loss of jobs, risk to humanity, possibility to be wrong, costly to develop, lack of original creativity, difficulty in handling highly intelligent tasks, lack of explanation and loss of skills etc. McCarthy has observed that today's nurses spend time doing low level tasks that can be performed by someone else with different skills. Nurses play an important role in every facet of patient care starting from the cost of health care to the overall patient's experience during hospital stay. Within this spectrum of responsibility lies the prospect for a number of different technologies to use the computing power of Artificial intelligence to assist with quality nursing care.
The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field. BACKGROUND INFORMATION Fostering trust in AI systems is a tremendous obstacle to bringing the most transformative AI technologies into reality, such as large-scale integration of machine intelligence in medicine. The challenge is to implement guiding ethical principles and aspirations and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the AI system. Meeting this challenge is critical to ensuring that medical professionals are prepared to correctly leverage AI in their practice and, ultimately, save lives. This research will concentrate on the influence AI applications have on the healthcare sector, its need, and its history in the medical field. Artificial intelligence models will assist doctors in various applications like patient care and administrative operations. (2011, March) Plant, R. et. al. According to the National Academies of Science, Engineering-diagnostic mistakes lead to roughly 10% of patient fatalities and 6 to 17% of hospital problems. It's crucial to remember that diagnostic errors aren't always caused by poor physician performance. Diagnostic mistakes, according to experts, are caused by a number of causes, including: • Collaboration and integration of health information technology are inefficient (Health IT) • Communication breakdowns between physicians, patients, and their families • A healthcare work system that is designed to be insufficiently supportive of diagnostic procedures. LITERATURE REVIEW Machine learning is being increasingly and frequently utilized in the healthcare field in various ways, like automating medical billing, clinical decision support, and establishing clinical care standards. Friedman, C., & Elhadad, N. (2014) et al. There are several significant applications of machine learning and healthcare ideas in medicine. The first medical machine learning system to diagnose acute toxicities in patients getting radiation treatment for head and neck malignancies has been created by researchers. In radiology, deep learning in healthcare automatically detects complicated patterns and assists radiologists in making informed judgments when analyzing pictures such as traditional radiography, CT, MRI, PET scans, and radiology reports. Machine learning-based automated detection and diagnostic systems have been demonstrated to perform as well as an expert radiologist. Google is creating a machine learning platform to identify breast cancer from images. These are only a handful of the numerous applications of machine learning in healthcare Jack Jr, C(2013) et.al. Natural Language Processing Nearly 80% of the information kept or "locked" in electronic health record systems is unstructured healthcare data for machine learning. These are papers or text files, not data components that could not previously be evaluated without a human viewing the information. Unfortunately, human language, often known as "natural language," is extremely complicated, lacks consistency, and contains many ambiguities, jargon, and vagueness. Therefore, machine learning in healthcare frequently uses natural language processing (NLP) tools to transform these papers into more usable and analyzable data.
Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.
ICEAT, 2019
Progress in computational sciences for cleaning, sorting, combining, digging, visualizing and managing data along with technological advancements in medical devices have urged needs for further extensive and consistent approaches to discuss the common key issues in medicine and health. Artificial Intelligence (AI) has significantly obtained grounds in everyday living in the era of information technology and it has now landed in healthcare. AI studies' in healthcare are evolving swiftly. However, it could only be the start of observing how it will influence patient care. AI tries to simulate human cognitive capacities. It is carrying a transformation pattern to healthcare, strengthened by the escalating availability of clinical data and sped up advancement in analytics systems. Nonetheless, there is a similar doubt, including some pressing warning at these elevated anticipations. This review examines the present state of AI applications in health, major developments in health AI, and the disparate consequences of health AI and offers some directions for institutions and caregivers utilizing AI techniques.
Journal of Propulsion Technology, 2023
The field of medicine has been revolutionised by uses of artificial intelligence (AI). Based on a review of the existing literature, this investigation delves deeper into the significance of artificial intelligence in healthcare, examining its impact in six key areas: There are many different kinds of administration software used in the healthcare industry, including (i) imaging and diagnostics, (ii) online patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other applications. The early diagnosis and containment of a coronavirus disease 2019 (COVID-19) outbreak, the provision of virtual patient care utilising AI-powered tools, the management of electronic health records, the improvement of patient engagement and compliance with the treatment plan, and the reduction of health care administrators' administrative workload are just a few examples of how artificial intelligence has made a significant impact on the healthcare industry. However, the scientific method includes AI into medical practise while simultaneously addressing a wide range of difficult logistical, moral, and sociological concerns.
Future Healthcare Journal, 2019
The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
International Journal of Engineering Materials and Manufacture
Artificial intelligence (AI) is the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data. This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligenc...
Open access journal of applied science and technology, 2024
The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.
Journal of Engineering Research and Reports, 2024
The application of artificial intelligence (AI) in healthcare is growing as it becomes more prevalent in modern business and everyday life. It is frequently regarded as a significant technological advancement in the present period. In recent times, the fields of artificial intelligence (AI) and big data analytics have been utilised in the domain of mobile health (m-health) to establish a highly efficient healthcare system. Modern medical research utilises diverse and poorly understood data, including electronic health records (EHRs), medical imaging, and complex language that is widely unorganised. The growth of mobile applications, together with healthcare systems, is a significant factor leading to the presence of disorganised and unstructured datasets. The enhanced accessibility of diverse datasets and advanced computer techniques like machine learning can enable researchers to usher in a new era of highly efficient genetic therapy. This review paper has clarified the role of machine learning algorithms in healthcare systems.
Journal of Healthcare Leadership
Artificial Intelligence (AI) and Machine Learning (ML) promise to transform all facets of medicine. Expected changes include more effective clinical triage, enhanced accuracy of diagnostic interpretations, improved therapeutic interventions, augmented workflow algorithms, streamlined data collection and processing, more precise disease prognostication, newer pharmacotherapies, and ameliorated genome interpretation. However, many caveats remain. Reliability of input data, interpretation of output data, data proprietorship, consumer privacy, and liability issues due to potential for data breaches will all have to be addressed. Of equal concern will be decreased human interaction in clinical care, patient satisfaction, affordability, and skepticism regarding cost-benefit. This descriptive literature-based treatise expounds on the promise and provisos associated with the anticipated import of AI and ML into all domains of medicine and healthcare in the very near future.
Artificial Intelligence (AI) is a digital technology that has been creating numerous breakthroughs across a variety of fields such as Gaming, Mathematics, Education, Natural Language Processing, Computer Vision, Robotics, healthcare, and so on. The field being dealt with in this article is healthcare which is still an emerging and if not, the most challenging one. This paper goes through a few breakthroughs of AI in healthcare from an industry and research perspective. Some of the ways how AI is being used across various healthcare domains are explained. The discussion goes on by briefing the future scope of AI and how it can make life simpler and more efficient for the stakeholders. Finally, it wraps up by uncovering ways in which AI for healthcare can still be a challenge to software companies and medical practitioners alike by addressing a few major challenges.
In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of health-care delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognised opportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.
Advanced Pharmaceutical Bulletin, 2020
The healthcare sector is considered to be one of the largest and fast-growing industries in the world. Innovations and novel approaches have always remained the prime aims in order to bring massive development. Before the emergence of technology, all the sectors, including the healthcare sector was dependant dependent on man power, which was time-consuming, and less accurate with lack of efficiency. With the recent advancements in machine learning, the condition is has been steadily revolutionizing. in the practice of the health care industry. Artificial Intelligence intelligence (AI) lies in the computer science department, which stresses on the intelligent machines’ creation, that work and react just like human beings. In simple words, AI is the capability of a computer program to think and learn, almost satisfying natural intelligence. It is the ability of a system to interpret the external data correctly, learn from it and finally use those learnings to execute some particular g...
Australasian Medical Journal, 2013
Zenodo (CERN European Organization for Nuclear Research), 2022
Purpose: Artificial Intelligence (AI) is the part of pc science that focuses on the creation of intelligent machines, wondering and working like humans. It's ushering in a new era in healthcare, fueled by the expanding availability of healthcare data. and fast development of analytics techniques. AI can be applied to quite a several kinds of healthcare information (structured and unstructured). Forms of Artificial Intelligence (AI), like deep mastering algorithms and neural networks, and classical guide vector desktop, are can be used for data that is organized. Natural language processing for unstructured data is also available. Using this information AI can be used to routinely spot issues and threats to patient safety, such as patterns of sub-most excellent care or outbreaks of hospital-acquired illness with excessive accuracy and speed. The goal of the research is to make a representation of the current usage of AI in healthcare and its future use. Approach: The detailed survey method on secondary data is used for analyzing the data.
Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency a...
Journal of Personalized Medicine
Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Never...
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