Background: The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. Methods: This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. Results: From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. Conclusion: Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

The purpose of this research is to investigate the moderating role of corporate governance on the relationship between earnings management and debt level in capital structure. The paper used a hypothesis-testing research approach to gather data from the annual reports of 13 industrial companies listed on Palestine Exchange and 25 Jordanian companies listed on Amman Stock Exchange from 2013 to 2020. Descriptive and inferential statistics were employed, along with correlation analysis to evaluate linear relationships between variables. The fixed and random effect regressions were utilized to develop the research model. In the case of Palestinian manufacturing firms, the results revealed that Earnings Management (EM) had a significant negative impact on debt level. According to the moderating role of Corporate Governance (CG), larger boards and the existence of female members on the board of directors causes an increase in the high-leverage impact of EM, whereas CEO duality mitigates the high-leverage impact. However, in the case of Jordanian manufacturing firms, EM showed an insignificant impact on debt level. Regarding the moderating role of CG, it was proved that the presence of female members on the board of directors increased the firm’s reliance on debt financing as a result of EM practices, while institutional investors mitigate the effect of EM on debt financing, leading to a decrease in reliance on debt. The findings of this research are suitable for the regulators while formulating policies on the Corporate Governance and the Impact of Earnings Management on Capital Structure. These findings have guided the policymakers that they should enhance their focus on Palestine and Jordan companies to test Corporate Governance Moderates the Impact of Earnings Management on Capital Structure. This study is also helpful for the new researcher while investigating this area in the future.

This study aims to predict corporate failure (CF) using the statistical forecasting method, employing R. It also seeks to investigate the impact of corporate governance mechanisms (CG) and the characteristics of board directors (BC) on CF. The study examined 480 annual reports from industrial and service firms listed on the Palestine and Jordan Stock Exchange markets, totaling 7,200 inputs. The research utilized a survival approach and the Cox hazard regression model. The study found a significant negative association between board size, board independence, board education, board age, firm size, and liquidity, considered together, and CF. Conversely, there is a significant positive association between profitability and CF. The log-likelihood test results indicated that the CG and BC models are significant. To the authors' knowledge, this study is the first in Palestine and Jordan to focus on the statistical prediction of financial failure, bridging a gap in the existing literature by understanding the association between governance indicators and board features on one hand, and financial failure on the other. Additionally, this study distinguishes itself as one of the pioneering studies within the accounting domain to adopt a survival approach, traditionally prevalent in medical research. It is also one of the few researches that use the R Language in the accounting and business field.