Artificial intelligence (AI)-enhanced medical drones in the healthcare supply chain (HSC) for sustainability development: A case study

Damoah, Isaac Sakyi; Ayakwah, Anthony; Tingbani, Ishmael

doi:10.1016/j.jclepro.2021.129598

Cited by 81 publications

(55 citation statements)

References 44 publications

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“…Other tasks performed were event detection, goal-driven optimization, interaction support, and personalization ( Table 2 , Multimedia Appendices 2 and 3 ). Although more than half (24/45, 53%) of the AI applications had no action autonomy [ 46 , 48 - 51 , 53 , 54 , 57 , 63 , 66 , 67 , 70 , 73 - 75 , 79 , 81 - 85 , 87 - 89 ], a few reported applications had low (2/21, 10%) [ 55 , 72 ], medium (4/21, 19%) [ 58 , 69 , 71 , 86 ], or high (6/21, 29%) [ 52 , 55 , 60 , 61 , 68 , 76 ] action autonomy ( Table 2 , Multimedia Appendices 2 and 3 ). Nearly three-quarters of all AI systems were intended for clinical care (33/45, 73%) [ 46 , 49 , 51 , 53 - 59 , 61 , 63 - 73 , 78 - 80 , 84 - 89 ], and the majority (18/33, 55%) of these concerned providing support to inform the patient-provider encounter [ 46 , 49 , 51 , 55 , 56 , 61 , 63 , 65 , 67 , 68 , 74 , 78 , …”

Section: Resultsmentioning

confidence: 99%

“…There was a wide range of study designs. Most (24/45, 53%) used a case-study design, including both single-case[46,49,50,52,53,[55][56][57]59,60,[66][67][68]70,74,75,[78][79][80][81][82][83]85,86] or multiple comparative case designs[53,56,78] (Table2and Multimedia Appendix 2).…”

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confidence: 99%

“…Overview of articles included in the scoping review (N=45).Case study Describe Child Health Improvement through Computer Automation system and methods to represent pediatric guidelines using Arden syntax Anand et al[79], 2018, United States; pediatrics Case study Conduct a detailed analysis of barriers to use of machine learning model in health care -time CDSS and its effect on adherence to clinical pathways Cruz et al[85], 2019, Spain; primary care Case study Explore how an AI-enhanced medical drone application in Ghana's health care supply chain improves the health care supply chain system Damoah et al[60], 2021, Ghana; management Case study Determine the impact of a machine learning algorithm, meant to mark CT d head examinations pending interpretation as higher probability for intracranial hemorrhage…”

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confidence: 99%

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Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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“…Most of the contemporary urban e-services are essentially exchanges of information, forms and agreements, and their digitalization process is, in principle, straightforward. However, due to the common lack of collective designing of public sector information technology system architectures, the task has proven to be more difficult in practice than anticipated [23,24].…”

Section: -Literature Background: Societal Issues and Ai In Urban Serv...mentioning

confidence: 99%

Public Perceptions on Application Areas and Adoption Challenges of AI in Urban Services

Yiğitcanlar

Inkinen

et al. 2022

Emerg Sci J

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Artificial intelligence (AI) deployment is exceedingly relevant to local governments, for example, in planning and delivering urban services. AI adoption in urban services, however, is an understudied area, particularly because there is limited knowledge and hence a research gap on the public's perceptions-users/receivers of these services. This study aims to examine people’s behaviors and preferences regarding the most suited urban services for application of AI technology and the challenges for governments to adopt AI for urban service delivery. The methodological approach includes data collection through an online survey from Australia and Hong Kong and statistical analysis of the data through binary logistic regression modeling. The study finds that: (a) Attitudes toward AI applications and ease of use have significant effects on forming an opinion on AI; (b) initial thoughts regarding the meaning of AI have a significant impact on AI application areas and adoption challenges; (c) perception differences between the two countries in AI application areas are significant; and (d) perception differences between the two countries in government AI adoption challenges are minimal. The study consolidates our understanding of how the public perceives the application areas and adoption challenges of AI, particularly in urban services, which informs local authorities that deploy or plan to adopt AI in their urban services. Doi: 10.28991ESJ-2022-06-06-01 Full Text: PDF

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“…Several studies have presented the applicability of I4T for sustainability in service firms. Damoah et al ( 2021 ) have examined the use of AI-drones to enhance the sustainability of healthcare supply chains in Ghana and found that use of AI-drones for emergency medical products have reduced the carbon footprint and noise pollution near hospitals. Consequently, this shall lead to better socio-economic situations and decreased death rates, thereby enhancing the sustainability of hospitals.…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Applying Industry 4.0 technologies for the sustainability of small service enterprises

Pandya

Kumar

2022

Serv Bus

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Despite the potential of Industry 4.0 (I4.0) to make micro, small and medium enterprises (MSMEs) sustainable, the managers of service MSMEs find it tough to implement it in their firms. Drawing on the TAM and TAM2, this paper identifies I4.0 technologies (I4T) that help improve the sustainability in service MSMEs in the emerging economy of India. Data from different service MSMEs were collected to assess the importance of I4T. Artificial Intelligence, Big Data Analytics, and Internet of Things emerge as the most important I4T for sustainability. Several managerial implications along with theoretical contributions, limitations, and future research have been discussed.

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Artificial intelligence (AI)-enhanced medical drones in the healthcare supply chain (HSC) for sustainability development: A case study

Cited by 81 publications

References 44 publications

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Public Perceptions on Application Areas and Adoption Challenges of AI in Urban Services

Applying Industry 4.0 technologies for the sustainability of small service enterprises

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