Australasian journal of engineering education, Год журнала: 2025, Номер unknown, С. 1 - 13
Опубликована: Фев. 21, 2025
Язык: Английский
International Journal of Medical Informatics, Год журнала: 2024, Номер 188, С. 105474 - 105474
Опубликована: Май 8, 2024
Язык: Английский
Процитировано
50
JMIR Medical Informatics, Год журнала: 2024, Номер 12, С. e53787 - e53787
Опубликована: Апрель 5, 2024
Background Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into medicine (EM) is growing, existing literature characterized a disparate collection individual studies, conceptual analyses, preliminary implementations. Given these complexities gaps understanding, cohesive framework needed to comprehend body knowledge on application EM. Objective absence comprehensive exploring roles EM, this scoping review aims systematically map LLMs’ applications within EM identify directions future research. Addressing gap will allow informed advancements field. Methods Using PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses extension Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web Science, Google Scholar papers published between January 2018 August 2023 that discussed use We excluded other forms AI. A total 1994 unique titles abstracts were screened, each full-text paper was independently reviewed 2 authors. Data abstracted independently, 5 authors performed collaborative quantitative qualitative synthesis data. Results 43 included. Studies predominantly from 2022 conducted United States China. uncovered four major themes: (1) decision-making support highlighted as pivotal area, with playing substantial role patient care, notably through their real-time triage, allowing early recognition urgency; (2) efficiency, workflow, information management demonstrated capacity significantly boost operational particularly automation record synthesis, which could reduce administrative burden enhance patient-centric care; (3) risks, ethics, transparency identified areas concern, especially regarding reliability outputs, specific studies challenges ensuring unbiased amidst potentially flawed training data sets, stressing importance thorough validation ethical oversight; (4) education communication possibilities included enrich medical training, such using simulated interactions skills. Conclusions have fundamentally transform decision-making, workflows, improving outcomes. This sets stage identifying key research areas: prospective LLM applications, establishing standards responsible use, understanding provider perceptions, physicians’ AI literacy. Effective integration require efforts evaluation ensure technologies can be safely effectively applied.
Язык: Английский
Процитировано
38
Journal of University Teaching and Learning Practice, Год журнала: 2024, Номер 21(06)
Опубликована: Апрель 19, 2024
Higher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, hype surrounding GenAI and increasing influence educational technology business groups over tertiary education. This commentary, prepared for Special Issue Journal University Teaching & Learning Practice (JUTLP) on “Enhancing student engagement using Artificial Intelligence (AI) chatbots,” delves into complex landscape opportunities threats that AI chatbots, including ChatGPT, introduce realm higher We argue while offers promise in enhancing pedagogy, research, administration, support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, regulatory gaps necessitate critical approach. Our commentary advocates development literacy among educators students, emphasising necessity foster an environment responsible innovation informed use AI. posit successful integration must be grounded principles ethics, equity, prioritisation aims human values. By offering nuanced exploration these issues, our contribute ongoing discourse how institutions can navigate rise GenAI, ensuring technological advancements benefit all stakeholders upholding core
Язык: Английский
Процитировано
37
Journal of Educational Evaluation for Health Professions, Год журнала: 2024, Номер 21, С. 6 - 6
Опубликована: Март 15, 2024
ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced highly complex responses. While holds promising applications medical education, its limitations potential risks cannot be ignored.
Язык: Английский
Процитировано
32
Medical Teacher, Год журнала: 2024, Номер 46(6), С. 752 - 756
Опубликована: Янв. 29, 2024
The custom GPT is the latest powerful feature added to ChatGPT. Non-programmers can create and share their own GPTs ("chat bots"), allowing Health Professions Educators apply capabilities of ChatGPT administrative assistants, online tutors, virtual patients, more, support clinical non-clinical teaching environments. To achieve this correctly, however, requires some skills, 12-Tips paper provides those: we explain how construct data sources, build relevant GPTs, basic security.
Язык: Английский
Процитировано
28
JMIR Medical Informatics, Год журнала: 2024, Номер 12, С. e54345 - e54345
Опубликована: Июль 3, 2024
Artificial intelligence (AI) chatbots have recently gained use in medical practice by health care practitioners. Interestingly, the output of these AI was found to varying degrees hallucination content and references. Such hallucinations generate doubts about their implementation.
Язык: Английский
Процитировано
27
Journal of Medical Systems, Год журнала: 2024, Номер 48(1)
Опубликована: Май 23, 2024
Язык: Английский
Процитировано
25
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
21
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
19
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
19