Generative Artificial Intelligence (Gen AI) in dental education: Opportunities, cautions, and recommendations.

Delgado-Ruiz R ，Kim AS ，Zhang H ，Sullivan D ，Awan KH ，Stathopoulou PG ... -  《-》

Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review.

Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications. This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners' skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.

Preiksaitis C ，Rose C 《-》

Generative artificial intelligence (Gen-AI) in pharmacy education: Utilization and implications for academic integrity: A scoping review.

Generative artificial intelligence (Gen-AI), exemplified by the widely adopted ChatGPT, has garnered significant attention in recent years. Its application spans various health education domains, including pharmacy, where its potential benefits and drawbacks have become increasingly apparent. Despite the growing adoption of Gen-AIsuch as ChatGPT in pharmacy education, there remains a critical need to assess and mitigate associated risks. This review exploresthe literature and potential strategies for mitigating risks associated with the integration of Gen-AI in pharmacy education. To conduct a scoping review to identify implications of Gen-AI in pharmacy education, identify its use and emerging evidence, with a particular focus on strategies which mitigate potential risks to academic integrity. A scoping review strategy was employed in accordance with the PRISMA-ScR guidelines. Databases searched includedPubMed, ERIC [Education Resources Information Center], Scopus and ProQuestfrom August 2023 to 20 February 2024 and included all relevant records from 1 January 2000 to 20 February 2024 relating specifically to LLM use within pharmacy education. A grey literature search was also conducted due to the emerging nature of this topic. Policies, procedures, and documents from institutions such as universities and colleges, including standards, guidelines, and policy documents, were hand searched and reviewed in their most updated form. These documents were not published in the scientific literature or indexed in academic search engines. Articles (n = 12) were derived from the scientific data bases and Records (n = 9) derived from the grey literature. Potential use and benefits of Gen-AI within pharmacy education were identified in all included published articles however there was a paucity of published articles related the degree of consideration to the potential risks to academic integrity. Grey literature recordsheld the largest proportion of risk mitigation strategies largely focusing on increased academic and student education and training relating to the ethical use of Gen-AI as well considerations for redesigning of current assessments likely to be a risk for Gen-AI use to academic integrity. Drawing upon existing literature, this review highlights the importance of evidence-based approaches to address the challenges posed by Gen-AI such as ChatGPT in pharmacy education settings. Additionally, whilst mitigation strategies are suggested, primarily drawn from the grey literature, there is a paucity of traditionally published scientific literature outlining strategies for the practical and ethical implementation of Gen-AI within pharmacy education. Further research related to the responsible and ethical use of Gen-AIin pharmacy curricula; and studies related to strategies adopted to mitigate risks to academic integrity would be beneficial.

Mortlock R ，Lucas C 《-》

Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations.

Generative AI (GenAI) tools like ChatGPT are increasingly relevant in dental education, offering potential enhancements in personalised learning and clinical reasoning. However, specific guidance from dental institutions remains unexplored. To identify, analyse and summarise existing guidelines from universities and organisations on using GenAI in dental education, focusing on recommendations for academic staff. A scoping review (10.17605/OSF.IO/3XMP7) searched for GenAI guidance on university websites, search engines (Google Search, Scholar, Perplexity and PubMed) and through contacting relevant academics (January 2022 to June 2024). Two reviewers independently screened and extracted data, including implementation details, AI tools and permitted/prohibited uses. Thematic analysis revealed common applications, benefits, challenges and recommendations. Thirty-one unique documents were included from 21 universities in 15 countries and three international organisations. Thematic analysis identified common applications, benefits, challenges and recommendations for integrating GenAI, including facilitating teaching and learning, personalised learning, efficient content creation and encouraging critical thinking. However, challenges such as academic integrity, ethical use, bias and privacy issues were also identified. No dental education-specific guidelines were found. This review identified and summarised existing GenAI guidelines from universities and organisations relevant to dental education. The guidelines emphasise ethical use, transparency, academic integrity, secure environments and AI misuse detection tools. However, the absence of dental specific guidance presents an opportunity to fill this gap, providing recommendations for academic staff to integrate GenAI effectively while promoting critical thinking and responsible AI use.

Uribe SE ，Maldupa I ，Schwendicke F 《-》

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.

Bhuyan SS ，Sateesh V ，Mukul N ，Galvankar A ，Mahmood A ，Nauman M ，Rai A ，Bordoloi K ，Basu U ，Samuel J ... -  《-》