Barriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review

Assadullah, Mir Mohammed

doi:10.2139/ssrn.3530598

Cited by 4 publications

(3 citation statements)

References 161 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…19 It is important to note that the rules around automated systems in healthcare are not yet clearly charted out: 'In the current legal accountability system there is no provision for a non-human actor.' 28 'Hence, in order to uphold moral responsibility and accountability of humans the European Group on Ethics requires 'meaningful human control' being maintained and that humans…”

Section: Mode Of Mhcmentioning

confidence: 99%

“…ultimately remain in control of the decision-making process.' 28 At the same time, the forms of control mentioned or those that are theoretically possible in each case must also be able to be actually implemented and achieve a control effect. As a condition for effectively (and in this sense meaningfully) exercised control, it is argued that human actors need sufficient time and must be able to sufficiently justify the reasons for exercising control.…”

Section: Extended Essaymentioning

confidence: 99%

See 1 more Smart Citation

Meaningful Human Control over AI for Health? A Review

Hille,

Hummel,

Braun

2023

J Med Ethics

View full text Add to dashboard Cite

Artificial intelligence is currently changing many areas of society. Especially in health, where critical decisions are made, questions of control must be renegotiated: who is in control when an automated system makes clinically relevant decisions? Increasingly, the concept of meaningful human control (MHC) is being invoked for this purpose. However, it is unclear exactly how this concept is to be understood in health. Through a systematic review, we present the current state of the concept of MHC in health. The results show that there is not yet a robust MHC concept for health. We propose a broader understanding of MHC along three strands of action: enabling, exercising and evaluating control. Taking into account these strands of action and the established rules and processes in the different health sectors, the MHC concept needs to be further developed to avoid falling into two gaps, which we have described as theoretical and labelling gaps.

show abstract

Section: Mode Of Mhcmentioning

confidence: 99%

Section: Extended Essaymentioning

confidence: 99%

Meaningful Human Control over AI for Health? A Review

Hille,

Hummel,

Braun

2023

J Med Ethics

View full text Add to dashboard Cite

show abstract

“…The butterfly optimization algorithm utilizing the Levy flight (BOALF) and modified butterfly optimization algorithm (BOARN) was proposed for detecting the pneumonia diseases [22]. The deep learning model using global average polling (GAP) techniques with pre-trained CNN was developed for the earlier detection of diabetic retinopathy [23]. The SVM [24] and data mining [25] is used for predicting the performance of students in the online learning process.…”

Section: Supervised Learning and Unsupervised Learningmentioning

confidence: 99%

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Sagiraju

Shashi

2021

IJEECS

View full text Add to dashboard Cite

Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).

show abstract

Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review

Hassan,

Kushniruk,

Borycki

2024

JMIR Hum Factors

View full text Add to dashboard Cite

Background Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders. Objective As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care. Methods A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O’Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. Results A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption. Conclusions The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients’ perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.

show abstract

Barriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review

Cited by 4 publications

References 161 publications

Meaningful Human Control over AI for Health? A Review

Meaningful Human Control over AI for Health? A Review

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review

Contact Info

Product

Resources

About