Application of Artificial Intelligence in Modern Healthcare System

Datta, Sudipto; Barua, Ranjit; Das, Jonali

doi:10.5772/intechopen.90454

Cited by 33 publications

(19 citation statements)

References 61 publications

(57 reference statements)

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“…Essentially, there exist 2 sorts of artificial intelligence machines: the first is the machine learning division, which analyzes structured data such as electrophysiological information, genetic information, and image analysis information. Machine learning systems in medical solutions attempt to collect patient distinctiveness or comprehend the probability of illness impacts [ 15 ]. Natural language processing technology is the next sort of AI device, and it may extract data from unconstrained or unorganized information such as clinical inspections or medical records in order to improve formatted medical screening information.…”

Section: Introductionmentioning

confidence: 99%

Research on Material Design of Medical Products for Elderly Families Based on Artificial Intelligence

Rong

Xin

et al. 2022

Applied Bionics and Biomechanics

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Due to the rapid growth of the elderly around the world, the artificial intelligence control framework can collect information and apply it and perform other tasks. Artificial intelligence plays an important role in focusing on the elderly. For example, it can improve the relationship between the elderly and family members or nursing teams. In addition, AI chat robot can communicate with the elderly without obstacles and can remind the elderly when to take medicine, regular physical examination, etc. A significant number of the AI applications on cell phones accessible today could screen wellbeing information, like every day exercises, diet, and surprisingly the senior’s way of life, in a less nosy way. In such cases, it could help in expecting and, subsequently, forestalling any conceivable hypertension or unpredictable heart rate. Essentially, mechanical ‘pets’ are likewise assisting with fighting off feelings of loneliness, while additionally assisting with upgrading patient consideration simultaneously. One model is Tombot, a little dog like model, which was made to diminish misery and tension among dementia patients. Its head developments, looks, and swaying tail feel basically the same as the real thing, causing occupants to feel as though they have their very own pet to really focus on. One of the issues that growing societies are presently facing is the care of elderly individuals. The dearth of skilled workers in the senior healthcare setting has been exacerbated by the worldwide shift of aging populations. There might be an enhanced need for old nursing since the global older demographic is expected to nearly triple in the coming three decades. There are advancements in computer technologies for supporting the aged plus associated caregivers, checking their wellbeing, and offering company to them. Given the global elderly demographic development possibilities, it is no coincidence that the aided care market is drawing fast advancement, rendering health management for nurses a breeze. While the world’s governments manage the aging population next years, these ideas will become extremely vital. They will almost certainly encounter economic and political demands to modify state medical care management, retirement benefits, and social security in order to meet the needs of an aging population. Considering that the demand for physicians is growing, a necessity has developed to deliver individualized care for the aged as well as to respond appropriately in emergencies. As a result, in the technological society, healthcare is exploring artificial intelligence to deliver personalized treatment to individuals in need. The challenges of the aged are determined in this study, and answers are supplied via a tailored computer (robot). With the crucial details given via Internet of Things gadgets, emergency events may be foreseen relatively promptly, and appropriate actions can be proposed by AI technology. Individuals’ vital health information is collected using the Internet of Things based on smart technology. The information is evaluated, and decisions are made by AI, while the developed machine performs the appropriate task. This research, therefore, looks at the material design of medical products for elderly people based on artificial intelligence. It goes further and explains some of the challenges encountered in the process and possible remedies.

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Section: Introductionmentioning

confidence: 99%

Research on Material Design of Medical Products for Elderly Families Based on Artificial Intelligence

Rong

Xin

et al. 2022

Applied Bionics and Biomechanics

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“…Due to the fast enhancement of the AI, it has been employed in various fields, such as the IoT [ 22 , 41 , 77 ], machine vision [ 78 ], driver assistance [ 79 , 80 ], and natural language processing [ 81 , 82 ]. AI has been put in application in various domains of healthcare [ 83 – 87 ] which includes cancer research [ 88 ], cardiology [ 89 ], diabetes [ 90 ], mental health [ 91 ], identification of prognosis [ 92 ], identification of Alzheimer's disease [ 93 ], identification of difference in the clinical groups [ 94 ], identification of cardiovascular disease [ 39 ], stroke-related studies [ 95 ], etc.…”

Section: Applications Of Artificial Intelligence In M-healthmentioning

confidence: 99%

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Khan

Alotaibi

2020

Journal of Healthcare Engineering

121

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Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

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“…Medical imaging is a fundamental part of the diagnosis and treatment of illnesses in clinical practices since it produces visual data of the human body. To date, AI is the best-performing technology in healthcare for the analysis of medical images and big data (Datta, Barua & Das, 2020). The impact of AI in this field is significant, especially because it assists clinicians in the analysis and interpretation of medical images, has great accuracy, enhances workflow, and reduces medical errors; in addition, it assists patients through the use of algorithms in devices such as smartwatches (Fingas, 2018;Victory, 2018), recording the patient data and making it available for processing and tracking (Topol, 2019).…”

Section: Convolutional Neural Network In Medical Analysismentioning

confidence: 99%

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

Ramakrishna

Hamid

Zaki

et al. 2020

PeerJ

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Stem cells are primitive and precursor cells with the potential to reproduce into diverse mature and functional cell types in the body throughout the developmental stages of life. Their remarkable potential has led to numerous medical discoveries and breakthroughs in science. As a result, stem cell–based therapy has emerged as a new subspecialty in medicine. One promising stem cell being investigated is the induced pluripotent stem cell (iPSC), which is obtained by genetically reprogramming mature cells to convert them into embryonic-like stem cells. These iPSCs are used to study the onset of disease, drug development, and medical therapies. However, functional studies on iPSCs involve the analysis of iPSC-derived colonies through manual identification, which is time-consuming, error-prone, and training-dependent. Thus, an automated instrument for the analysis of iPSC colonies is needed. Recently, artificial intelligence (AI) has emerged as a novel technology to tackle this challenge. In particular, deep learning, a subfield of AI, offers an automated platform for analyzing iPSC colonies and other colony-forming stem cells. Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition. CNNs are able to distinguish cells with high accuracy based on morphologic and textural changes. Therefore, CNNs have the potential to create a future field of deep learning tasks aimed at solving various challenges in stem cell studies. This review discusses the progress and future of CNNs in stem cell imaging for therapy and research.

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Application of Artificial Intelligence in Modern Healthcare System

Cited by 33 publications

References 61 publications

Research on Material Design of Medical Products for Elderly Families Based on Artificial Intelligence

Research on Material Design of Medical Products for Elderly Families Based on Artificial Intelligence

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

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