scholarly journals Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes

2021 ◽  
Vol 8 ◽  
pp. 238212052110368
Author(s):  
Joel Grunhut ◽  
Adam TM Wyatt ◽  
Oge Marques
Keyword(s):  
Artificial Intelligence ◽  
Medical Students ◽  
Technology Acceptance ◽  
Medical Curriculum ◽  
Integrative Review ◽  
Human Machine Interaction ◽  
Students Attitudes ◽  
Acceptance Model ◽  
Meta Analyses ◽  
Machine Interaction

BACKGROUND As medicine and the delivery of healthcare enters the age of Artificial Intelligence (AI), the need for competent human–machine interaction to aid clinical decisions will rise. Medical students need to be sufficiently proficient in AI, its advantages to improve healthcare's expenses, quality, and access. Similarly, students must be educated about the shortfalls of AI such as bias, transparency, and liability. Overlooking a technology that will be transformative for the foreseeable future would place medical students at a disadvantage. However, there has been little interest in researching a proper method to implement AI in the medical education curriculum. This study aims to review the current literature that covers the attitudes of medical students towards AI, implementation of AI in the medical curriculum, and describe the need for more research in this area. METHODS An integrative review was performed to combine data from various research designs and literature. Pubmed, Medline (Ovid), GoogleScholar, and Web of Science articles between 2010 and 2020 were all searched with particular inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were successively pooled together, recorded, and analyzed quantitatively using a modified Hawkings evaluation form. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was utilized to help improve reporting. RESULTS A total of 39 articles meeting inclusion criteria were identified. Primary assessments of medical students attitudes were identified (n = 5). Plans to implement AI in the curriculum for the purpose of teaching students about AI (n = 6) and articles reporting actual implemented changes (n = 2) were assessed. Finally, 26 articles described the need for more research on this topic or calling for the need of change in medical curriculum to anticipate AI in healthcare. CONCLUSIONS There are few plans or implementations reported on how to incorporate AI in the medical curriculum. Medical schools must work together to create a longitudinal study and initiative on how to successfully equip medical students with knowledge in AI.

scholarly journals Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Pierre Auloge ◽  
Julien Garnon ◽  
Joey Marie Robinson ◽  
Sarah Dbouk ◽  
Jean Sibilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Interventional Radiology ◽  
Clinical Practice ◽  
Medical Students ◽  
Medical Curriculum ◽  
New Technology ◽  
Large Population ◽  
The Other ◽  
Clinical Phase ◽  
Preclinical Phase

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

scholarly journals Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior

2021 ◽  
Author(s):  
Svenja Mohr ◽  
Rainer Kühl
Keyword(s):  
Artificial Intelligence ◽  
Theory Of Planned Behavior ◽  
Technology Acceptance ◽  
Planned Behavior ◽  
Technology Acceptance Model ◽  
Structural Equation ◽  
Perceived Behavioral Control ◽  
Labor Costs ◽  
Online Questionnaire ◽  
Acceptance Model

AbstractThe use of Artificial Intelligence (AI) in agriculture is expected to yield advantages such as savings in production resources, labor costs, and working hours as well as a reduction in soil compaction. However, the economic and ecological benefits of AI systems for agriculture can only be realized if farmers are willing to use them. This study applies the technology acceptance model (TAM) of Davis (1989) and the theory of planned behavior (TPB) of Ajzen (1991) to investigate which behavioral factors are influencing the acceptance of AI in agriculture. The composite model is extended by two additional factors, expectation of property rights over business data and personal innovativeness. A structural equation analysis is used to determine the importance of factors influencing the acceptance of AI systems in agriculture. For this purpose, 84 farmers were surveyed with a letter or an online questionnaire. Results show that the perceived behavioral control has the greatest influence on acceptance, followed by farmers’ personal attitude towards AI systems in agriculture. The modelled relationships explain 59% of the total variance in acceptance. Several options and implications on how to increase the acceptance of AI systems in agriculture are discussed.

scholarly journals EARLY STUDY OF TEACHING & LEARNING ADAPTION BY USING MULTIMEDIA IN TEXT LEARNING OF SULALATUS SALATIN

2021 ◽  
Vol 3 (8) ◽  
pp. 167-181
Author(s):  
Nurul Shuhada Abdul Aziz ◽  
Norazimah Zakaria
Keyword(s):  
Technology Acceptance ◽  
Student Attitudes ◽  
Teaching And Learning ◽  
Text Learning ◽  
The Third ◽  
Teacher Skills ◽  
Students Attitudes ◽  
Acceptance Model ◽  
Teaching Learning ◽  
The Relationship

This study aims to examine the use of multimedia in teaching and learning (PdP) Sulalatus Salatin in form six. The objective of this study is to identify the use of multimedia in PdP Sulalatus Salatin in terms of types, tools used, teacher skills, and curriculum needs. The second objective is to study students 'attitudes towards the use of multimedia in Sulalatus Salatin learning and the third to analyze the relationship between multimedia use in PdPc with students' understanding of Sulalatus Salatin text using the Technology Acceptance Model (TAM). The researcher distributed a set of questionnaire questions containing 36 questions that were closed to 30 forms six students at a school in the district of Kuala Kangsar, Perak. The findings show that the main reason for the use of multimedia in PdP Sulalatus Salatin in terms of multimedia type, tools used, teacher skills, and curriculum needs is because the use of LCD screen causes the text to appear larger and clearer with a mean score of 4.433 (sd = 0.8976). For the second objective, the two most dominant student attitudes with a mean score of 4,400 that is with the use of multimedia, writing in the text of Sulalatus Salatin is clearer and easier to read (sd = 0.7701), while students are actively involved during the teaching of Sulalatus Salatin text (sd = 0.8944). Next, for the third objective, the relationship between the use of multimedia with the highest level of students' understanding of Sulalatus Salatin text is that students are easier to read and understand the synopsis on Sulalatus Salatin text with a mean score of 4,600 (sd = 0.6215). In conclusion, this study was implemented to provide exposure to the advantages of multimedia used by teachers while teaching Sulalatus Salatin text in form six.

How AI chatbots have reshaped the frontline interface in China: examining the role of sales–service ambidexterity and the personalization–privacy paradox

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hua Fan ◽  
Bing Han ◽  
Wei Gao ◽  
Wenqian Li
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods Research ◽  
Online Survey ◽  
Polynomial Regression ◽  
Customer Experience ◽  
Human Machine Interaction ◽  
Content Type ◽  
Positive Effects ◽  
Privacy Paradox ◽  
Machine Interaction

PurposeThis study serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox.Design/methodology/approachAn online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim.FindingsThe results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage.Originality/valueFirst, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.

Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?

2021 ◽  
pp. 147572572110371
Author(s):  
Sabrina Gado ◽  
Regina Kempen ◽  
Katharina Lingelbach ◽  
Tanja Bipp
Keyword(s):  
Artificial Intelligence ◽  
Technology Acceptance ◽  
Human Perception ◽  
Psychology Students ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Technology Acceptance Models ◽  
Psychological Training ◽  
Acceptance Model ◽  
And Behavior

Psychologists with their expertise in statistics and regarding human perception and behavior can contribute valuable insights to the development of innovative and useful artificial intelligence (AI) systems. Therefore, we need to raise attention and curiosity for AI and foster the willingness to engage with it among psychology students. This requires identifying approaches to integrate a general understanding of AI technology into formal psychological training and education. This study investigated to what extent psychology students currently accept and use AI and what affects their perception and usage. Therefore, an AI acceptance model based on established technology acceptance models was developed and tested in a sample of 218 psychology students. An acceptable fit with the data was found for an adapted version. Perceived usefulness and ease of use were most predictive for the students’ attitude towards AI; attitude itself, as well as perceived usefulness, social norm, and perceived knowledge, were predictors for the intention to use AI. In summary, we identified relevant factors for designing AI training approaches in psychology curricula. In this way, possible restraints regarding the use of AI can be reduced and its beneficial opportunities exploited in psychological contexts.

Synchronous Physiological Electrical Fields

2016 ◽  
pp. 107-127
Author(s):  
J.F. Pagel
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Processing ◽  
Current Knowledge ◽  
Human Machine Interaction ◽  
Similar Frequency ◽  
Electrical Fields ◽  
Electrical Potentials ◽  
Interface Potential ◽  
External Stimuli ◽  
Machine Interaction

Humans utilize sensory and motor systems developed genetically, physically and socially for interfacing with our external environment. We use these same systems to interface in our interactions with artificial intelligence. There are other functioning central nervous system (CNS) systems, however, involved in cognitive processing for which the function and environmental interface is less clear. The synchronous physiologic electrical field system utilizes broadcast extracellular electrical fields for a wide variety of CNS functions. The operations of this system are usually non-conscious and most apparent during sleep (especially the conscious states of sleep that include dreaming), and un-focused waking. The electrical fields of this system are altered and affected by both internal and external stimuli. These fields can be monitored and analyzed by artificial intelligence (AI) systems, and independently of human input, AI systems can utilize similar frequency based electrical potentials to convey data, communicate, supply power, and to store memory. From both human and AI perspectives, these systems have the potential to function more fully in human/machine interaction. This chapter reviews our current knowledge as to function, current interactive approaches, and interface potential for these physiological electrical fields.

Ambient Intelligence Environments

2011 ◽  
pp. 92-98 ◽  
Author(s):  
Carlos Ramos
Keyword(s):  
Artificial Intelligence ◽  
Embedded Systems ◽  
Ambient Intelligence ◽  
New World ◽  
Physical World ◽  
Human Being ◽  
Human Machine Interaction ◽  
Artificial Intelligence Research ◽  
Essential Knowledge ◽  
Machine Interaction

The trend in the direction of hardware cost reduction and miniaturization allows including computing devices in several objects and environments (embedded systems). Ambient Intelligence (AmI) deals with a new world where computing devices are spread everywhere (ubiquity), allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours. AmI environments may be so diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, transports, touristic attractions, stores, sport installations, and music devices. Ambient Intelligence involves many different disciplines, like automation (sensors, control, and actuators), human-machine interaction and computer graphics, communication, ubiquitous computing, embedded systems, and, obviously, Artificial Intelligence. In the aims of Artificial Intelligence, research envisages to include more intelligence in the AmI environments, allowing a better support to the human being and the access to the essential knowledge to make better decisions when interacting with these environments

scholarly journals Technology acceptance model based study of students’ attitudes toward use of enterprise resource planning solutions

2019 ◽  
Vol 43 (1) ◽  
pp. 49-71 ◽  
Author(s):  
Simona Sternad Zabukovšek ◽  
Ruben Picek ◽  
Samo Bobek ◽  
Irena Šišovska Klančnik ◽  
Polona Tominc
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Enterprise Resource Planning ◽  
Personal Characteristics ◽  
Software Tool ◽  
Resource Planning ◽  
Perceived Support ◽  
Students Attitudes ◽  
Study Process ◽  
Acceptance Model

Enterprise Resource Planning (ERP) solutions are the most frequently used software tool in companies in all industries. Therefore, the labour market requires the knowledge and skills for usage of ERP solutions from graduates – future employees. The main objective of our paper is therefore the identification of important factors that contribute to the acceptance of ERP solutions by students in economics and business and that shape their intentions to use this knowledge in the future. The model of our research is based on the Technology Acceptance Model (TAM), extended by identified important multidimensional external factors that refer to (1) students’ personal characteristics and information literacy, (2) perceived system and technological characteristics of ERP solutions and (3) perceived support within the study process. Research results revealed that several dimensions of the three multidimensonal factors play an important role in shaping the attitudes towards acceptance of ERP solutions by students.

An intelligent interface for online interaction

1984 ◽  
Vol 9 (1) ◽  
pp. 7-18 ◽  
Author(s):  
A. Vickery
Keyword(s):  
Artificial Intelligence ◽  
Online Interaction ◽  
Search System ◽  
Human Machine Interaction ◽  
Retrieval Systems ◽  
Intelligent Interface ◽  
Machine Interaction

In this paper, the author discusses the ways of improving the performance of online retrieval systems by introducing an automated interface between the enquirer and the system. In the first part of the paper, the main features of such human/machine interaction and the characteristics that the user would like to see incorporated in an interface, are de scribed. Then, studies in artificial intelligence that are particu larly relevant to the problems of implementing an intelligent interface, are discussed. The author concludes with a summary of automated mechanisms that will be needed to improve the quality of interaction between the user and the search system.

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