Development of a Medical AI Ethics Education Module Reflecting AI Competency Framework for Medical Students

Korean Medical Education Review, Journal Year: 2025, Volume and Issue: 27(1), P. 17 - 25

Published: Feb. 28, 2025

As artificial intelligence (AI) technologies advance and become increasingly integrated into medicine healthcare services, there is a growing consensus that it necessary to prepare medical students understand utilize AI in education. Research discussions are ongoing regarding the competencies professionals should have. There diverse opinions on how integrate for graduates existing curricula. However, wide agreement exists importance of providing sufficient appropriate education ethical aspects using clinical practice research. In this paper, authors aim introduce practical educational principles, strategies, methods educators interested teaching ethics medicine. To achieve this, paper (1) introduces school possess; (2) explains necessity fostering education; (3) discusses principles developing considerations implementing such curricula; (4) presents modules can be utilized cultivate young physicians. The hope case-based module we have developed may contribute who familiar with guidelines ethics, enabling them make best choices any given environment.

Language: Английский

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Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Maternal health remains a critical global challenge, with disparities in access to care and quality of services contributing high maternal mortality morbidity rates. Artificial intelligence (AI) has emerged as promising tool for addressing these challenges by enhancing diagnostic accuracy, improving patient monitoring, expanding care. This review explores the transformative role AI healthcare, focusing on its applications early detection pregnancy complications, personalized care, remote monitoring through AI-driven technologies. tools such predictive analytics machine learning can help identify at-risk pregnancies guide timely interventions, reducing preventable neonatal complications. Additionally, AI-enabled telemedicine virtual assistants are bridging healthcare gaps, particularly underserved rural areas, accessibility women who might otherwise face barriers Despite potential benefits, data privacy, algorithmic bias, need human oversight must be carefully addressed. The also discusses future research directions, including globally ethical frameworks integration. holds revolutionize both accessibility, offering pathway safer, more equitable outcomes.

Language: Английский

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Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study

Published: Aug. 16, 2024

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed examine specific fears, anxieties, mistrust, and ethical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 studying across various academic years, employing structured self-administered questionnaire an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, Ethics comprising 12 items, three items for each construct. The results indicated variable levels anxiety genAI among participating students: 34.1% reported no role their future careers (n = 56), while 41.5% slightly anxious 61), 22.0% somewhat 36), 2.4% extremely 4). Among constructs, Mistrust was most agreed upon (mean: 12.35±2.78), followed by construct 10.86±2.90), Fear 9.49±3.53), Anxiety 8.91±3.68). Sex, level, Grade Point Average (GPA) did not significantly affect students’ perceptions However, there notable direct association between general elevated scores constructs scale. Prior exposure previous use modify These findings highlighted critical need refined educational strategies address integration training. demonstrated pervasive anxiety, fear, regarding deployment healthcare, indicating necessity curriculum modifi-cations that focus specifically these areas. Interventions should be tailored increase familiarity competency, which would alleviate apprehension equip physicians engage this inevitable technology effectively. study also importance incorporating discussions into courses mistrust human-centered aspects Conclusively, calls proactive evolution education prepare AI-driven healthcare practices shortly ensure well-prepared, confident, ethically informed professional interactions technologies.

Language: Английский

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Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight

Academic Pathology, Journal Year: 2025, Volume and Issue: 12(1), P. 100166 - 100166

Published: Jan. 1, 2025

The integration of artificial intelligence in pathology has ignited discussions about the role technology diagnostics-whether serves as a tool for augmentation or risks replacing human expertise. This manuscript explores intelligence's evolving contributions to pathology, emphasizing its potential capacity enhance, rather than eclipse, pathologist's role. Through historical comparisons, such transition from analog digital radiology, this paper highlights how technological advancements have historically expanded professional capabilities without diminishing essential element. Current applications pathology-from diagnostic standardization workflow efficiency-demonstrate augment accuracy, expedite processes, and improve consistency across institutions. However, challenges remain algorithmic bias, regulatory oversight, maintaining interpretive skills among pathologists. discussion underscores importance comprehensive governance frameworks, educational curricula, public engagement initiatives ensure remains collaborative endeavor that empowers professionals, upholds ethical standards, enhances patient outcomes. ultimately advocates balanced approach where expertise work concert advance future medicine.

Language: Английский

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Will you help AI? A longitudinal study on the relationship between interacting with chatbots and altruistic willingness towards AI

Behaviour and Information Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: March 19, 2025

Language: Английский

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Management Practices, Faculty Self-Efficacy, and Institutional Performance: A Narrative Review

Innovare Journal of Education, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10

Published: May 1, 2025

The relationship between management practices, faculty self-efficacy, and institutional performance is a critical area of study in higher education research. Effective practices provide strategic leadership, governance, resource allocation that influence experiences efficiency. Faculty defined as educators’ belief their ability to teach, conduct research, engage activities, serves mediating factor determines how well strategies translate into academic success. Institutional performance, measured through indicators such effectiveness, efficiency, equity, transparency, accountability, sustainability, reflects the overall success an institution fulfilling its mission adapting evolving educational landscapes. These three constructs interact dynamic reciprocal manner, where shape confidence, engagement drives outcomes, influences future experiences. This literature review explores interrelationship among these constructs, drawing from empirical studies theoretical frameworks analyze leadership approaches, development programs, governance structures affect self-efficacy findings suggest institutions with strong high tend perform better student learning research productivity, stakeholder satisfaction. However, gaps remain understanding long-term impact on cross-cultural variations strategies, role digital transformation shaping relationships. Addressing will valuable insights for leaders seeking enhance effectiveness evidence-based policies support initiatives.

Language: Английский

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Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial

Published: May 2, 2025

Background and aims Clinical competency is paramount for nurses to ensure that patients receive safe, high-quality care. Generative artificial intelligence (GenAI) in nursing education gaining attention, evidence shows its suitability real-life situations. GenAI may be an effective solution enhancing nurses’ clinical competency. This study compared the impact of scenario-based patient simulation versus immersive 360° virtual reality (VR) on educational outcomes, namely competence, cultural awareness, AI readiness, effectiveness. Methods cross-over randomised controlled design was conducted from June 2024 August 2024. Forty-four undergraduate students years 1, 2, 3 were selected participate. Subgroups formed, each comprising three different years. They either a (intervention, Group B) or VR (control, A) separate days with washout period. Four self-reported questionnaires used measure competency: Competence Questionnaire (CCQ), Cultural Awareness Scale (CAS), Medical Artificial Intelligence Readiness Students (MAIRS-MS), Simulation Effectiveness Tool – Modified (SET-M). Results The revealed notable improvements competence confidence among participants. A demonstrated significant enhancements CCQ at both time points, B also showed meaningful progress. Both groups experienced changes CAS-Total scores, although these not statistically significant. In terms MAIRS-MS total score, had increase 1 (T1), improvement baseline 2 (cross-over session, T2). Regarding SET-M results, most participants (75%) felt debriefing contributed their learning, 77.3% reported increased assessment skills. Conclusions findings offer compelling effectiveness as assessed by CCQ, CAS, MAIRS-MS. Importantly, our results reveal measures, particularly within B. real-time feedback can serve powerful teaching tools improving students’ outcomes; however, exhibits notably greater effect. trial registration/number Not applicable

Language: Английский

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Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study

BMC Medical Education, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 24, 2025

Abstract Background Generative Artificial Intelligence (GAI) has significantly impacted education at all levels, including health professional education. Understanding students’ experiences is essential to enhancing AI literacy, adapting GAI, and implementing GAI technology. Therefore, the aim was explore physiotherapy of thoughts on in their education, its potential implications for future careers healthcare. Methods Qualitative descriptive design. Focus groups were conducted, using a semi-structured interview guide, Physiotherapy program Linköping University, Sweden, from March April 2024. The 15 students organized into three focus groups, one each year. data analyzed inductive content analysis. Results An overarching theme “GAI—Great if navigating challenges” emerged categories: 1) “Areas use learning process”: Students viewed as tool introduction inspiration, assimilating course clinical reasoning problem-solving; 2) “Optimizing education”: found be timesaving, tailored, virtual study partner teacher. They discussed pros cons learning, concerns permitted usage, need critical approach, how individual interests influenced interactions with GAI; 3) “Future profession”: believed would more reliable, subject-specific models enhance care delivery, but also pose risks related profit motives knowledge gaps. Conclusion beneficial expressed about impact quality. emphasized importance approach when organizational support, supporting use. that advanced could provide accurate reliable educational tools healthcare documentation evidence-based decision-making. However, include business Navigating these challenges fully leveraging GAI’s benefits practice. fostering ensuring robust support crucial maximizing positive physiotherapy.

Language: Английский

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Designing Personalized Multimodal Mnemonics With AI: A Medical Student’s Implementation Tutorial

JMIR Medical Education, Journal Year: 2025, Volume and Issue: 11, P. e67926 - e67926

Published: May 8, 2025

Abstract Background Medical education can be challenging for students as they must manage vast amounts of complex information. Traditional mnemonic resources often follow a standardized approach, which may not accommodate diverse learning styles. Objective This tutorial presents student-developed approach to creating personalized multimodal mnemonics (PMMs) using artifical intelligence tools. Methods demonstrates structured implementation process ChatGPT (GPT-4 model) text generation and DALL-E 3 visual creation. We detail the prompt engineering framework, including zero-shot, few-shot, chain-of-thought prompting techniques. The involves (1) template development, (2) refinement, (3) personalization, (4) specification, (5) quality control. time typically ranges from 2 5 minutes per concept, with 1 iterations needed optimal results. Results Through systematic testing across 6 medical concepts, achieved an initial success rate 85%, improving 95% after refinement. Key challenges included maintaining accuracy (addressed through specific terminology in prompts), ensuring clarity (improved anatomical specifications), achieving integration visuals (resolved review protocols). provides practical templates, troubleshooting strategies, control measures address common challenges. Conclusions offers framework tools artificial intelligence. By following detailed measures, efficiently generate customized while avoiding pitfalls. emphasizes human oversight iterative refinement ensure educational value. elimination need developing separate databases streamlines process.

Language: Английский

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Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study

International Medical Education, Journal Year: 2024, Volume and Issue: 3(4), P. 406 - 425

Published: Oct. 9, 2024

Despite the potential benefits of generative artificial intelligence (genAI), concerns about its psychological impact on medical students, especially job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine specific fears, anxieties, mistrust, and ethical students harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 studying across various academic years, employing structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, Ethics—comprising 12 items, 3 items for each construct. Exploratory confirmatory factors analyses assess construct validity scale. The results indicated variable levels anxiety genAI among participating students: 34.1% reported no genAI‘s role their future careers (n = 56), while 41.5% slightly anxious 61), 22.0% somewhat 36), 2.4% extremely 4). Among constructs, Mistrust was most agreed upon (mean: 12.35 ± 2.78), followed by Ethics 10.86 2.90), Fear 9.49 3.53), Anxiety 8.91 3.68). Their sex, level, Grade Point Average (GPA) did not significantly affect students’ perceptions However, there notable direct association between general elevated scores constructs Prior exposure previous use modify These findings highlight critical need refined educational strategies address integration into training. demonstrate anxiety, fear, regarding deployment healthcare, indicating necessity curriculum modifications that focus specifically these areas. Interventions should be tailored increase familiarity competency genAI, which would alleviate apprehensions equip physicians engage this inevitable technology effectively. study also highlights importance incorporating discussions courses mistrust human-centered aspects In conclusion, calls proactive evolution education prepare new AI-driven healthcare practices ensure well prepared, confident, ethically informed professional interactions technologies.

Language: Английский

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