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.

Background Clinical training during the COVID-19 pandemic is high risk for medical students. Medical schools in low- and middle-income countries (LMIC) have limited capacity to develop resources in the face of rapidly developing health emergencies. Here, a free Massive Open Online Course (MOOC) was developed as a COVID-19 resource for medical students working in these settings, and its effectiveness was evaluated. Methods The RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework was utilized to evaluate the effectiveness of MOOC in teaching medical students about COVID-19. The data sources included the student registration forms, metrics quantifying their interactions within the modules, students’ course feedback, and free-text responses. The data were collected from the Moodle learning management system and Google analytics from May 9 to September 15, 2020. The research team analyzed the quantitative data descriptively and the qualitative data thematically. Results Among the 16,237 unique visitors who accessed the course, only 6031 medical students from 71 medical schools registered, and about 4993 (83% of registrants) completed the course, indicating high levels of satisfaction (M = 8.17, SD = 1.49) on a 10-point scale. The mean scores of each assessment modules were > 90%. The free-text responses from 987 unique students revealed a total of 17 themes (e.g., knowing the general information on COVID-19, process management of the pandemic in public health, online platform use, and instructional design) across the elements of the RE-AIM framework. Mainly, the students characterized the MOOC as well-organized and effective. Conclusions Medical students learned about COVID-19 using a self-paced and unmonitored MOOC. MOOCs could play a vital role in the dissemination of accurate information to medical students in LMIC in future public health emergencies. The students were interested in using similar MOOCs in the future.

Background Artificial intelligence (AI) has affected our day-to-day in a great extent. Healthcare industry is one of the mainstream fields among those and produced a noticeable change in treatment and education. Medical students must comprehend well why AI technologies mediate and frame their decisions on medical issues. Formalizing of instruction on AI concepts can facilitate learners to grasp AI outcomes in association with their sensory perceptions and thinking in the dynamic and ambiguous reality of daily medical practice. The purpose of this study is to provide consensus on the competencies required by medical graduates to be ready for artificial intelligence technologies and possible applications in medicine and reporting the results. Materials and methods A three-round e-Delphi survey was conducted between February 2020 and November 2020. The Delphi panel accorporated experts from different backgrounds; (i) healthcare professionals/ academicians; (ii) computer and data science professionals/ academics; (iii) law and ethics professionals/ academics; and (iv) medical students. Round 1 in the Delphi survey began with exploratory open-ended questions. Responses received in the first round evaluated and refined to a 27-item questionnaire which then sent to the experts to be rated using a 7-point Likert type scale (1: Strongly Disagree—7: Strongly Agree). Similar to the second round, the participants repeated their assessments in the third round by using the second-round analysis. The agreement level and strength of the consensus was decided based on third phase results. Median scores was used to calculate the agreement level and the interquartile range (IQR) was used for determining the strength of the consensus. Results Among 128 invitees, a total of 94 agreed to become members of the expert panel. Of them 75 (79.8%) completed the Round 1 questionnaire, 69/75 (92.0%) completed the Round 2 and 60/69 (87.0%) responded to the Round 3. There was a strong agreement on the 23 items and weak agreement on the 4 items. Conclusions This study has provided a consensus list of the competencies required by the medical graduates to be ready for AI implications that would bring new perspectives to medical education curricula. The unique feature of the current research is providing a guiding role in integrating AI into curriculum processes, syllabus content and training of medical students.

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This chapter is structured to help give readers a general impression about gamification and its relationship with e-learning from the perspective of basic sources of gamification. Gamification is important for e-learning, which can share the same digital environment with digital game components because it has the potential to increase the engagement of the learners in digital environment. This chapter on gamification has primarily tried to explain the reasons for prompting people to play and the kinds of actions of the players after defining the game and gamification. Afterwards, explaining the components of the gamification design models, some gamification applications are stated as successful examples that can be used in e-learning. The chapter concludes by giving information about the limits, criticism, and future of gamification.