Use of Artificial Intelligence in Healthcare Delivery

Reddy, Sandeep

doi:10.5772/intechopen.74714

Cited by 45 publications

(21 citation statements)

References 42 publications

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“…Communication and reasoning between patients and professionals and establishing a professional and therapeutic relationship is hard to replace with AI. Therefore, a cohabitation between AI and professionals has been suggested (Reddy, ). However, the risks of being technical and professionally focused are still there and need to be accounted for in the development of such models.…”

Section: Discussionmentioning

confidence: 99%

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Patient participation in municipal elderly care from the perspective of nurses and occupational therapists

Lidström-Holmqvist

James

2019

Nursing Open

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Aim The aim of this study was to describe how nurses and occupational therapists in municipal care of older people define and implement patient participation in their daily work. Design This study had a cross‐sectional design. Data were collected using an online questionnaire. Methods The questionnaire had both closed and open‐ended questions. One‐hundred and fourteen nurses and occupational therapists responded. Data were analysed with descriptive statistics and thematic analysis. Results Two main themes were identified as follows: “The professionals’ perspective at the centre – Patient participation to enhance compliance” and “The patients’ perspective at the centre – Patient participation as an ongoing process.” The themes covered a continuum. On one extreme, patient participation was equated with making the patient comply with what the professionals wanted to do. On the other extreme, all power was transferred to the patient. The first theme was restricted to the decision‐making process. The second theme covered the entire care or, rehabilitation, process.

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Section: Discussionmentioning

confidence: 99%

“…It is seen as one of the solutions to the future challenges in health care with a growing older population. However, AI cannot easily replace the communication and professional relationship between patient and professional (Reddy, ), meaning challenges when it comes to patient participation.…”

Section: Introductionmentioning

confidence: 99%

Patient participation in municipal elderly care from the perspective of nurses and occupational therapists

Lidström-Holmqvist

James

2019

Nursing Open

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“…In the field of health care, the implementation of AI technologies fosters the prediction, diagnosis, and treatment of diseases, which benefits both patients and health care providers [ 7 ]. The promise of improving diagnostic accuracy is one of AI's most exciting health care applications.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Health Care: Bibliometric Analysis

Guo¹,

Hao²,

Zhao³

et al. 2020

J Med Internet Res

276

153

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Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

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“…For example, in the blockchain network, it will be important to consider what law might apply to transactions and what appropriate risk management should be put into place [117]. Regarding AI, it might be easier to create some forms of legal schemes and internal governance models that will dictate the governing law for AI operations in healthcare [118]. Specially, we also consider legal issues about content, personal information running on blockchain and AI platforms, such as problems with copyright infringement and defamation.…”

Section: A Challengesmentioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

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Use of Artificial Intelligence in Healthcare Delivery

Cited by 45 publications

References 42 publications

Patient participation in municipal elderly care from the perspective of nurses and occupational therapists

Patient participation in municipal elderly care from the perspective of nurses and occupational therapists

Artificial Intelligence in Health Care: Bibliometric Analysis

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

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