A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities

Ali, Omar; Abdelbaki, Wiem; Shrestha, Anup; Elbaşi, Ersin; Alryalat, Mohammad Abdallah Ali; Dwivedi, Yogesh K.

doi:10.1016/j.jik.2023.100333

Cited by 204 publications

(80 citation statements)

References 121 publications

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“…2019; Ali et al. 2023). Therefore, it was imperative to study such a context where AI is at the infancy stage to understand its long‐lasting impact on various workplace outcomes.…”

Section: Methodsmentioning

confidence: 99%

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Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being

Shaikh

Afshan

Anwar

et al. 2023

Asia Pac J Human Res

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Following social cognitive theory, the current study investigated the impact of artificial intelligence (AI) on employees' productivity in the healthcare sector. AI significantly facilitates the management of hospitals to vigilantly assess employees’ productivity and accurately analyze employees' characteristics, such as attitude, emotion and behavior. With the underlying mechanism of employee mental health and well‐being, and knowledge sharing, the study has considered beneficial and harmful perspectives of AI in the workplace. The study also hypothesizes the important moderating role of technological leadership. The data was collected from 184 doctors in Pakistan's major hospitals. Partial least squares (PLS) results support a direct relationship between AI and employee productivity. The findings also supported the underlying mechanism of knowledge sharing and mental health and well‐being in the relationship between AI and employee productivity. However, the technological leadership moderating effect was found to be insignificant. It opens an important avenue for this further research and future directions.

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“…2019; Ali et al. 2023). Therefore, it was imperative to study such a context where AI is at the infancy stage to understand its long‐lasting impact on various workplace outcomes.…”

Section: Methodsmentioning

confidence: 99%

“…Second, the healthcare sector is at an early stage of adopting artificial intelligence technologies in a developing country such as Pakistan (Abid et al 2019;Ali et al 2023). Therefore, it was imperative to study such a context where AI is at the infancy stage to understand its long-lasting impact on various workplace outcomes.…”

Section: Research Contextmentioning

confidence: 99%

Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being

Shaikh

Afshan

Anwar

et al. 2023

Asia Pac J Human Res

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“…According to Ali et al ( 2023), the expanding human population will lead to a further rise in the demand for healthcare services all over the globe. As a result, creative AI solutions are necessary to increase the efficacy and efficiency of health services without resulting in new financial burdens (Ali et al, 2023). According to research, healthcare institutions who invest in artificial intelligence technology may be able to enhance the health results of their patients.…”

Section: The History Of Artificial Intelligence In the Medical Fieldmentioning

confidence: 99%

The Contributions of Information and Communications Technology on the Sustainable Development of Artificial Intelligence in the Medical Field

Han¹

2023

JID

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This review provides an analysis and critique of current research on the uses of artificial intelligence in the medical field (AI). This chapter starts out by providing a definition of artificial intelligence from the perspective of communications research. As a consequence of this, it addresses recent advancements in AI as well as the influence that information and communications technology (ICT) has had on the use of AI in the medical field. In conclusion, it discusses the challenges that are impeding the industry's progress toward more advanced forms of artificial intelligence over the long term. The purpose of this study is to ascertain whether or not the incorporation of innovative information and communication technology (ICT) has a favorable effect on prospective applications of AI in the medical business. As a consequence of this, the review paper provides the conceptual groundwork for conducting an investigation into the ways in which AI and ICT are involved in the medical field.

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“…Knowledge sharing among researchers and clinical experts may be prone to information privacy issues. Hence, a proven strategy is a prerequisite to overcoming these challenges and establishing the dominance of AI over the healthcare industry in the future 2 …”

Section: Introductionmentioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities

Cited by 204 publications

References 121 publications

Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being

Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being

The Contributions of Information and Communications Technology on the Sustainable Development of Artificial Intelligence in the Medical Field

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

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