Exploring Medical Students' and Faculty's Perception on Artificial Intelligence and Robotics. A Questionnaire Survey

Abstract Background With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning. Materials We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. Results The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. Conclusion The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

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Symbiosis with artificial intelligence via the prism of law, robots, and society

Artificial Intelligence and Law ◽

10.1007/s10506-021-09289-1 ◽

2021 ◽

Author(s):

Stamatis Karnouskos

Keyword(s):

Artificial Intelligence ◽

Social Economic ◽

Autonomous Robots ◽

Law And Society ◽

Citizenship Rights ◽

Legal Entity ◽

Profound Impact ◽

The People ◽

Physical Presence ◽

And Robotics

AbstractThe rapid advances in Artificial Intelligence and Robotics will have a profound impact on society as they will interfere with the people and their interactions. Intelligent autonomous robots, independent if they are humanoid/anthropomorphic or not, will have a physical presence, make autonomous decisions, and interact with all stakeholders in the society, in yet unforeseen manners. The symbiosis with such sophisticated robots may lead to a fundamental civilizational shift, with far-reaching effects as philosophical, legal, and societal questions on consciousness, citizenship, rights, and legal entity of robots are raised. The aim of this work is to understand the broad scope of potential issues pertaining to law and society through the investigation of the interplay of law, robots, and society via different angles such as law, social, economic, gender, and ethical perspectives. The results make it evident that in an era of symbiosis with intelligent autonomous robots, the law systems, as well as society, are not prepared for their prevalence. Therefore, it is now the time to start a multi-disciplinary stakeholder discussion and derive the necessary policies, frameworks, and roadmaps for the most eminent issues.

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

Insights into Imaging ◽

10.1186/s13244-020-00942-y ◽

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.

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Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study

BMC Medical Education ◽

10.1186/s12909-021-02546-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ozan Karaca ◽

S. Ayhan Çalışkan ◽

Kadir Demir

Keyword(s):

Artificial Intelligence ◽

Factor Analysis ◽

Medical Students ◽

Data Science ◽

Factor Model ◽

Health Science ◽

Measurement Tool ◽

Validity And Reliability ◽

Mathematical Concepts ◽

Evaluation And Monitoring

Abstract Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students’ required competencies on AI, a diverse set of experts’ opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach’s alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

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Effective learning strategies for Japanese Kampo medicine using problem‐based learning and simulator training for medical students: A questionnaire survey

Traditional & Kampo Medicine ◽

10.1002/tkm2.1281 ◽

2021 ◽

Author(s):

Marie Amitani ◽

Haruka Amitani ◽

Hajime Suzuki ◽

Suguru Kawazu ◽

Takamasa Fukumoto ◽

...

Keyword(s):

Medical Students ◽

Learning Strategies ◽

Questionnaire Survey ◽

Problem Based Learning ◽

Simulator Training ◽

Kampo Medicine ◽

Effective Learning

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Designing an Artificial Intelligence Control Program Model to be Tested and Implemented in Virtual Reality for Automated Chevrolet Camaro

Proceedings ◽

10.3390/proceedings2020063044 ◽

2020 ◽

Vol 63 (1) ◽

pp. 44

Author(s):

Lavinia Andrei ◽

Doru-Laurean Baldean ◽

Adela-Ioana Borzan

Keyword(s):

Artificial Intelligence ◽

Virtual Reality ◽

Control Program ◽

Specific Model ◽

Virtual Model ◽

Program Model ◽

Real Size ◽

Virtual Modeling ◽

And Robotics ◽

Virtual Reality Application

A control program was designed with Unity 5 virtual reality application in the automotive and robotics field. Thus, a virtual model of a robotic car was tested in a virtual reality program. After optimization, the smart controller was implemented on a specific model of the automated Chevrolet Camaro. The main objective of the present paper is to design a control program model to be tested in virtual reality and in a real-size car. Results concerning the virtual modeling of an automated car and its artificial intelligence controls have been presented and discussed, outlining the forces, torques, and context awareness capabilities of the car.

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