BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)
Опубликована: Сен. 9, 2024
Язык: Английский
BMC Medical Education, Год журнала: 2025, Номер 25(1)
Опубликована: Янв. 17, 2025
The rapid advancement of artificial intelligence (AI) has revolutionized both medical education and healthcare by delivering innovative tools that enhance learning improve overall outcomes. study aimed to assess students' perceptions regarding the credibility effectiveness AI as a tool explore dynamics integrating in education. A cross-sectional was carried out across colleges Pakistan. 26-question survey developed using Google Forms from previously validated studies. assessed demographics participants, basic understanding AI, socio-ethical impacts use AI. data analyzed SPSS (v 26.0) derive descriptive inferential statistics. total 702 students aged 18 26 years (mean age 20.50 ± 1.6 years) participated study. findings revealed generally favorable attitude towards among (80.3%), with majority considering it an effective (60.8%) credible (58.4%) Students agreed optimized their time (60.3%) provided up-to-date information (63.1%). Notably, 65.7% found more efficient helping them grasp concepts compared traditional like books lectures, while 66.8% reported receiving accurate answers inquiries through highlighted view becoming increasingly outdated (59%), emphasizing importance into creating dedicated (80%) for This demonstrated is education, offering personalized experiences improved educational are learn cutting down on study-time, providing answers, ultimately improving We recommend developing formal integration curricula, along appropriate regulatory oversight ensure can human abilities rather than acting replacement humans.
Язык: Английский
Процитировано
3
Cogent Education, Год журнала: 2025, Номер 12(1)
Опубликована: Янв. 3, 2025
The emergence of Generative Artificial Intelligence (AI) marks a revolutionary advancement in education. This study explores the profound impact implementing AI on teachers' teaching performance, with focus enhancing effectiveness and pedagogical practices. research uses survey methodology, employing proportionated stratified random sampling technique. A total 466 participants, consisting teachers, were involved this study, questionnaires serving as primary tool for data collection. analysis method used was Structural Equation Model (SEM). Research indicates that significantly enhances performance by improving ease use, usefulness, learning. Teacher perceptions AI's usability influence its integration into student-focused learning, learning material development, practice enhancement. Additionally, is crucial adoption. Alongside these promising opportunities, also highlights challenges need to be addressed successful education, such technical limitations necessity teacher training. By exploring application depth, offers valuable insights leveraging technology foster more inclusive, personalized, practical education digital age.
Язык: Английский
Процитировано
2
JMIR Medical Education, Год журнала: 2023, Номер 9, С. e50373 - e50373
Опубликована: Дек. 11, 2023
The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues transform various aspects our lives, one critical question arises for medical education: what will be the nature education, teaching, learning in a world where acquisition, retention, application knowledge traditional sense are fundamentally altered by AI?
Язык: Английский
Процитировано
26
JMIR Formative Research, Год журнала: 2024, Номер 8, С. e51346 - e51346
Опубликована: Март 21, 2024
Large language models (LLMs) are computational artificial intelligence systems with advanced natural processing capabilities that have recently been popularized among health care students and educators due to their ability provide real-time access a vast amount of medical knowledge. The adoption LLM technology into education training has varied, little empirical evidence exists support its use in clinical teaching environments.
Язык: Английский
Процитировано
14
BMC Medical Education, Год журнала: 2024, Номер 24(1)
Опубликована: Июль 9, 2024
Abstract Background Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate effectiveness employing large models, particularly ChatGPT, improving English academic skills these students. Methods A cohort 25 third-year students from China was recruited. The consisted two stages. Firstly, were asked write mini paper. Secondly, revise using ChatGPT within weeks. evaluation papers focused on three key dimensions, including structure, logic, language. method incorporated both manual scoring AI utilizing ChatGPT-3.5 ChatGPT-4 models. Additionally, we employed questionnaire gather feedback students’ experience ChatGPT. Results After implementing assistance, there notable increase by 4.23 points. Similarly, based model showed an 4.82 points, while 3.84 These results highlight potential models supporting writing. Statistical analysis revealed no difference between scoring, indicating assist teachers grading process. Feedback indicated generally positive response with 92% acknowledging improvement quality their writing, 84% noting advancements skills, 76% recognizing contribution research. Conclusion highlighted efficacy like augmenting proficiency non-native speakers education. Furthermore, it illustrated make educational process, environments where primary
Язык: Английский
Процитировано
13
medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown
Опубликована: Янв. 22, 2024
Abstract Background The rapid advancement of generative artificial intelligence (AI) has led to the wide dissemination models with exceptional understanding and generation human language. Their integration into healthcare shown potential for improving medical diagnostics, yet a comprehensive diagnostic performance evaluation AI comparison their that physicians not been extensively explored. Methods In this systematic review meta-analysis, search Medline, Scopus, Web Science, Cochrane Central, MedRxiv was conducted studies published from June 2018 through December 2023, focusing on those validate tasks. risk bias assessed using Prediction Model Study Risk Bias Assessment Tool. Meta-regression performed summarize compare accuracy physicians. Results resulted in 54 being included meta-analysis. Nine were evaluated across 17 specialties. quality assessment indicated high majority studies, primarily due small sample sizes. overall 56.9% (95% confidence interval [CI]: 51.0–62.7%). meta-analysis demonstrated that, average, exceeded (difference accuracy: 14.4% [95% CI: 4.9–23.8%], p-value =0.004). However, both Prometheus (Bing) GPT-4 showed slightly better compared non-experts (-2.3% -27.0–22.4%], = 0.848 -0.32% -14.4–13.7%], 0.962), but underperformed when experts (10.9% -13.1–35.0%], 0.356 12.9% 0.15–25.7%], 0.048). sub-analysis revealed significantly improved fields Gynecology, Pediatrics, Orthopedic surgery, Plastic Otolaryngology, while showing reduced Neurology, Psychiatry, Rheumatology, Endocrinology General Medicine. No significant heterogeneity observed based bias. Conclusions Generative exhibits promising capabilities, varying by model specialty. Although they have reached reliability expert physicians, findings suggest enhance delivery education, provided are integrated caution limitations well-understood. Key Points Question: What is how does physicians? Findings: This found pooled interval: exceeds all specialties, however, some comparable non-expert Meaning: suggests do match level experienced may applications education.
Язык: Английский
Процитировано
12
Digital Government Research and Practice, Год журнала: 2024, Номер unknown
Опубликована: Авг. 3, 2024
This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with AI. The competencies range from foundational AI prompt engineering programming skills, including ethical legal considerations. These twelve offer framework individuals, policymakers, government officials, educators looking navigate take advantage of the potential responsibly. Embedding these into educational programs professional training initiatives can equip individuals become responsible informed users creators follow logical progression serve as roadmap seeking get familiar researchers policymakers develop assessments, programs, guidelines, regulations.
Язык: Английский
Процитировано
12
Annals of Surgical Oncology, Год журнала: 2024, Номер 31(10), С. 6387 - 6393
Опубликована: Июнь 22, 2024
Язык: Английский
Процитировано
11
Medicine, Год журнала: 2024, Номер 103(31), С. e38955 - e38955
Опубликована: Авг. 2, 2024
This narrative review examined the intersection of generative artificial intelligence (GAI) and personalization health professional education (PHE). aims to elucidate current condition GAI technologies their particular uses in field PHE. Data were extracted analyzed from studies focusing on demographics development preferences healthcare workers, competencies required for personalized precision medicine, potential applications (AI) The also addressed ethical implications AI implementation this context. Findings indicated a gender-balanced workforce with predisposition toward continuous digital tool utilization. A need comprehensive educational framework was identified include spectrum skills crucial emphasizing importance patient involvement bioethics. found enhance experiences research PHE, an increasing trend applications, particularly surgical since 2018. Ethical challenges associated integration PHE highlighted, emphasis design diverse teams. Core concepts established, spotlight emerging areas such as data science learning analytics. application recognized its benefits future advancements, call vigilance. holds significant promise personalizing frameworks developer teams address bias equity applications.
Язык: Английский
Процитировано
11
Graefe s Archive for Clinical and Experimental Ophthalmology, Год журнала: 2024, Номер unknown
Опубликована: Сен. 15, 2024
Язык: Английский
Процитировано
11