Modeling adoption of intelligent agents in medical imaging

Calisto, Francisco Maria; Nunes, Nuno; Nascimento, Jacinto C.

doi:10.1016/j.ijhcs.2022.102922

Cited by 57 publications

(27 citation statements)

References 90 publications

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“…Assessing the positive influence of the factors, it was observed that EE and facilitating conditions were identified to be strong influence factors. These findings indicate that the perceived ease of use or convenience and usability of AI assistants among the participants is high, which can also be correlated with facilitating conditions that provide support in using the assistants in similar studies [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 57%

Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia

Alanzi,

Alotaibi,

Alajmi

et al. 2023

Cureus

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Background: Artificial intelligence (AI) is a novel technology that has been widely acknowledged for its potential to improve the processes' efficiency across industries. However, its barriers and facilitators in healthcare are not completely understood due to its novel nature. Study purpose: The purpose of this study is to explore the intricate landscape of AI use in family medicine, aiming to uncover the factors that either hinder or enable its successful adoption. Methods: A cross-sectional survey design is adopted in this study. The questionnaire included 10 factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, personal innovativeness, ethical concerns, and facilitators) affecting the acceptance of AI. A total of 157 family physicians participated in the online survey. Results: Effort expectancy (μ = 3.85) and facilitating conditions (μ = 3.77) were identified to be strong influence factors. Access to data (μ = 4.33), increased computing power (μ = 3.92), and telemedicine (μ = 3.78) were identified as major facilitators; regulatory support (μ = 2.29) and interoperability standards (μ = 2.71) were identified as barriers along with privacy and ethical concerns. Younger individuals tend to have more positive attitudes and expectations toward AI-enabled assistants compared to older participants (p < .05). Perceived privacy risk is negatively correlated with all factors. Conclusion: Although there are various barriers and concerns regarding the use of AI in healthcare, the preference for AI use in healthcare, especially family medicine, is increasing.

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Section: Discussionmentioning

confidence: 57%

Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia

Alanzi,

Alotaibi,

Alajmi

et al. 2023

Cureus

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“…In addition, the treatment of “Privacy Concern,” “Risk,” “Security,” and “Trust” acknowledges the multifaceted nature of users’ perceptions, which mediate their adoption decisions (Arfi et al. , 2021; Calisto et al. , 2022; Choudhury et al.…”

Section: Resultsmentioning

confidence: 99%

Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry

Cobelli,

Blasi

2024

EJIM

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PurposeThis paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.Design/methodology/approachWe followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.FindingsOur results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.Research limitations/implicationsThe study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.Practical implicationsATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.Originality/valueThe originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

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“…R2 is the measurement of the independent variables’ abilities to interpret dependent variables. When the R2 value is close to 1, the explanatory abilities of independent variables are at higher levels [ 63 , 64 , 65 ]. Among all models, the most explanatory is LightGBM, followed by XGBoost and CatBoost, and finally DT and RF.…”

Section: Numerical Resultsmentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

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The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

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Modeling adoption of intelligent agents in medical imaging

Cited by 57 publications

References 90 publications

Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia

Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia

Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

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