Cureus Journal of Computer Science., Год журнала: 2025, Номер unknown
Опубликована: Июнь 5, 2025
Язык: Английский
BMC Research Notes, Год журнала: 2025, Номер 18(1)
Опубликована: Фев. 24, 2025
This study aimed to design and develop a virtual patient program using generative Artificial Intelligence (AI) technology, providing medical students opportunities practice history-taking with chatbot. We evaluated the feasibility of this approach by analyzing quality responses generated Five expert reviewers participated in pilot test, interacting chatbot take history presenting urinary problem Korean AI platform Naver HyperCLOVA X®. They five-item questionnaire rated on five-point Likert scale. The 96 pairs questions answers, totaling 1,325 words 177 sentences. Discourse analysis scripts revealed that 2.6% (34) were deemed implausible categorized into inarticulate hallucinations, missing important information. Participants answers as relevant (M = 4.50 ± 0.32), valid 4.20 0.40), accurate 4.10 0.20), succinct 3.80 0.51), but neutral about their fluency 3.20 0.60). Using for patients is feasible, improvements are needed more articulate natural responses.
Язык: Английский
Процитировано
0
Electronics, Год журнала: 2025, Номер 14(5), С. 1028 - 1028
Опубликована: Март 5, 2025
Medical education is rapidly evolving with the integration of artificial intelligence (AI), particularly through application generative AI to create dynamic learning environments. This paper examines transformative role prompt engineering in enhancing simulation-based emergency medicine. By enabling generation realistic, context-specific clinical case scenarios, fosters critical thinking and decision-making skills among medical trainees. To guide systematic implementation, we introduce PROMPT+ Framework, a structured methodology for designing, evaluating, refining prompts AI-driven simulations, while incorporating essential ethical considerations. Furthermore, emphasize importance developing specialized models tailored regional guidelines, standard operating procedures, educational contexts ensure relevance alignment current standards practices. The framework aims provide approach engaging AI-generated content, allowing learners reflect on reasoning, critically assess recommendations, consider potential tools training workflows. Additionally, acknowledge certain challenges associated use education, such as maintaining reliability addressing biases outputs. Our study explores how simulations could contribute scalability adaptability potentially offering methods healthcare professionals engage contexts.
Язык: Английский
Процитировано
0
Forum for education studies., Год журнала: 2025, Номер 3(2), С. 2730 - 2730
Опубликована: Апрель 1, 2025
This paper explores the transformative role of artificial intelligence (AI) in medical education, emphasizing its as a pedagogical tool for technology-enhanced learning. highlights AI’s potential to enhance learning process various inquiry-based strategies and support Competency-Based Medical Education (CBME) by generating high-quality assessment items with automated personalized feedback, analyzing data from both human supervisors AI, helping predict future professional behavior current trainees. It also addresses inherent challenges limitations using AI student assessment, calling guidelines ensure valid ethical use. Furthermore, integration into virtual patient (VP) technology offer experiences encounters significantly enhances interactivity realism overcoming conventional VPs. Although incorporating chatbots VPs is promising, further research warranted their generalizability across clinical scenarios. The discusses preferences Generation Z learners suggests conceptual framework on how integrate teaching supporting learning, aligning needs today’s students utilizing adaptive capabilities AI. Overall, this areas education where can play pivotal roles overcome educational offers perspectives developments education. calls advance theory practice tools innovate practices tailored understand long-term impacts AI-driven environments.
Язык: Английский
Процитировано
0
Currents in Pharmacy Teaching and Learning, Год журнала: 2025, Номер 17(6), С. 102348 - 102348
Опубликована: Апрель 4, 2025
Язык: Английский
Процитировано
0
Academic Radiology, Год журнала: 2025, Номер unknown
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Cogent Education, Год журнала: 2025, Номер 12(1)
Опубликована: Май 15, 2025
Язык: Английский
Процитировано
0
Frontiers in Artificial Intelligence, Год журнала: 2025, Номер 8
Опубликована: Май 21, 2025
Язык: Английский
Процитировано
0
Journal of Health Sciences and Medicine, Год журнала: 2025, Номер 8(3), С. 524 - 528
Опубликована: Май 30, 2025
Aims: This study aims to evaluate the usefulness and reliability of artificial intelligence (AI) applications in thoracic surgery internship education exam preparation. Methods: Claude Sonnet 3.7 AI was provided with core topics covered 5th-year instructed generate a 20-question multiple-choice exam, including an answer key. Four specialists assessed AI-generated questions using Delphi panel method, classifying them as correct, minor error, or major error. Major errors included absence correct among choices, incorrect AI-marked answers, contradictions established medical knowledge. A second manually created by specialist evaluated same methodology. Seven volunteer students completed both exams, correlation between their scores statistically analyzed. Results: Among questions, 8 (40%) contained errors, while 1 (5%) had The expert-generated perfect accuracy rate, whereas significantly lower (p=0.001). Median were 75 (67-100) for 85 (70-95) expert exam. No significant found students’ (r=0.042, p=0.929). Conclusion: high error rate (40% major, 5% minor), making unreliable unsupervised use education. While may provide partial benefits under supervision, it currently lacks required independent implementation
Язык: Английский
Процитировано
0
Cureus Journal of Computer Science., Год журнала: 2025, Номер unknown
Опубликована: Июнь 5, 2025
Язык: Английский
Процитировано
0