PT	AU	BA	BE	GP	AF	BF	CA	TI	SO	SE	BS	LA	DT	CT	CY	CL	SP	HO	DE	ID	AB	C1	C3	RP	EM	RI	OI	FU	FP	FX	CR	NR	TC	Z9	U1	U2	PU	PI	PA	SN	EI	BN	J9	JI	PD	PY	VL	IS	PN	SU	SI	MA	BP	EP	AR	DI	DL	D2	EA	PG	WC	WE	SC	GA	PM	OA	HC	HP	DA	UT
J	Abi-Rafeh, J; Xu, HH; Kazan, R; Tevlin, R; Furnas, H				Abi-Rafeh, Jad; Xu, Hong Hao; Kazan, Roy; Tevlin, Ruth; Furnas, Heather			Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT	AESTHETIC SURGERY JOURNAL												Background The rapidly evolving field of artificial intelligence (AI) holds great potential for plastic surgeons. ChatGPT, a recently released AI large language model (LLM), promises applications across many disciplines, including healthcare.Objectives The aim of this article was to provide a primer for plastic surgeons on AI, LLM, and ChatGPT, including an analysis of current demonstrated and proposed clinical applications.Methods A systematic review was performed identifying medical and surgical literature on ChatGPT's proposed clinical applications. Variables assessed included applications investigated, command tasks provided, user input information, AI-emulated human skills, output validation, and reported limitations.Results The analysis included 175 articles reporting on 13 plastic surgery applications and 116 additional clinical applications, categorized by field and purpose. Thirty-four applications within plastic surgery are thus proposed, with relevance to different target audiences, including attending plastic surgeons (n = 17, 50%), trainees/educators (n = 8, 24.0%), researchers/scholars (n = 7, 21%), and patients (n = 2, 6%). The 15 identified limitations of ChatGPT were categorized by training data, algorithm, and ethical considerations.Conclusions Widespread use of ChatGPT in plastic surgery will depend on rigorous research of proposed applications to validate performance and address limitations. This systemic review aims to guide research, development, and regulation to safely adopt AI in plastic surgery.					宏浩, 徐/HKE-7858-2023	Abi-Rafeh, Jad/0000-0002-7483-1515													1090-820X	1527-330X				FEB 15	2024	44	3					329	343		10.1093/asj/sjad260	http://dx.doi.org/10.1093/asj/sjad260		SEP 2023						37562022					WOS:001066826600001
J	Sorin, V; Brin, D; Barash, Y; Konen, E; Charney, A; Nadkarni, G; Klang, E				Sorin, Vera; Brin, Dana; Barash, Yiftach; Konen, Eli; Charney, Alexander; Nadkarni, Girish; Klang, Eyal			Large Language Models and Empathy: Systematic Review	JOURNAL OF MEDICAL INTERNET RESEARCH												Background: Empathy, a fundamental aspect of human interaction, is characterized as the ability to experience another being's emotions within oneself. In health care, empathy is a fundamental for health care professionals and patients' interaction. It is a unique quality to humans that large language models (LLMs) are believed to lack. Objective: We aimed to review the literature on the capacity of LLMs in demonstrating empathy. Methods: We conducted a literature search on MEDLINE, Google Scholar, PsyArXiv, medRxiv, and arXiv between December 2022 and February 2024. We included English-language full-length publications that evaluated empathy in LLMs' outputs. We excluded papers evaluating other topics related to emotional intelligence that were not specifically empathy. The included studies' results, including the LLMs used, performance in empathy tasks, and limitations of the models, along with studies' metadata were summarized. Results: A total of 12 studies published in 2023 met the inclusion criteria. ChatGPT-3.5 (OpenAI) was evaluated in all studies, with 6 studies comparing it with other LLMs such GPT-4, LLaMA (Meta), and fine-tuned chatbots. Seven studies focused on empathy within a medical context. The studies reported LLMs to exhibit elements of empathy, including emotions recognition and emotional support in diverse contexts. Evaluation metric included automatic metrics such as Recall-Oriented Understudy for Gisting Evaluation and Bilingual Evaluation Understudy, and human subjective evaluation. Some studies compared performance on empathy with humans, while others compared between different models. In some cases, LLMs were observed to outperform humans in empathy-related tasks. For example, ChatGPT-3.5 was evaluated for its responses to patients' questions from social media, where ChatGPT's responses were preferred over those of humans in 78.6% of cases. Other studies used subjective readers' assigned scores. One study reported a mean empathy score of 1.84-1.9 (scale 0-2) for their fine-tuned LLM, while a different study evaluating ChatGPT-based chatbots reported a mean human rating of 3.43 out of 4 for empathetic responses. Other evaluations were based on the level of the emotional awareness scale, which was reported to be higher for ChatGPT-3.5 than for humans. Another study evaluated ChatGPT and GPT-4 on soft-skills questions in the United States Medical Licensing Examination, where GPT-4 answered 90% of questions correctly. Limitations were noted, including repetitive use of empathic phrases, difficulty following initial instructions, overly lengthy responses, sensitivity to prompts, and overall subjective evaluation metrics influenced Conclusions: LLMs exhibit elements of cognitive empathy, recognizing emotions and providing emotionally supportive responses in various contexts. Since social skills are an integral part of intelligence, these advancements bring LLMs closer to room for improvement in both the performance of these models and the evaluation strategies used for assessing soft skills.					; mirzaei, hamed/X-2374-2018; Sorin, Vera/IAR-4247-2023; Barash, Yiftach/MCK-5975-2025; Brin, Dana/MVU-5184-2025	Brin, Dana/0009-0003-7316-206X; Sorin, Vera/0000-0003-0509-4686; Barash, Yiftach/0000-0002-7242-1328; 													1438-8871					DEC 11	2024	26								e52597	10.2196/52597	http://dx.doi.org/10.2196/52597								39661968					WOS:001382690300002
J	Feuerriegel, S; Maarouf, A; Bär, D; Geissler, D; Schweisthal, J; Pröllochs, N; Robertson, CE; Rathje, S; Hartmann, J; Mohammad, SM; Netzer, O; Siegel, AA; Plank, B; Van Bavel, JJ				Feuerriegel, Stefan; Maarouf, Abdurahman; Baer, Dominik; Geissler, Dominique; Schweisthal, Jonas; Proellochs, Nicolas; Robertson, Claire E.; Rathje, Steve; Hartmann, Jochen; Mohammad, Saif M.; Netzer, Oded; Siegel, Alexandra A.; Plank, Barbara; Van Bavel, Jay J.			Using natural language processing to analyse text data in behavioural science	NATURE REVIEWS PSYCHOLOGY												Language is a uniquely human trait at the core of human interactions. The language people use often reflects their personality, intentions and state of mind. With the integration of the Internet and social media into everyday life, much of human communication is documented as written text. These online forms of communication (for example, blogs, reviews, social media posts and emails) provide a window into human behaviour and therefore present abundant research opportunities for behavioural science. In this Review, we describe how natural language processing (NLP) can be used to analyse text data in behavioural science. First, we review applications of text data in behavioural science. Second, we describe the NLP pipeline and explain the underlying modelling approaches (for example, dictionary-based approaches and large language models). We discuss the advantages and disadvantages of these methods for behavioural science, in particular with respect to the trade-off between interpretability and accuracy. Finally, we provide actionable recommendations for using NLP to ensure rigour and reproducibility.					; Siegel, Alexandra/M-1331-2019; Feuerriegel, Stefan/ABD-6599-2021; Netzer, Oded/KHW-9415-2024; Hartmann, Jochen/IUN-2216-2023; Bavel, Jay/I-6748-2015	Schweisthal, Jonas/0000-0003-3725-3821; 														2731-0574				FEB	2025	4	2					96	111		10.1038/s44159-024-00392-z	http://dx.doi.org/10.1038/s44159-024-00392-z		JAN 2025											WOS:001386522700001
J	Nasra, M; Jaffri, R; Pavlin-Premrl, D; Kok, HK; Khabaza, A; Barras, C; Slater, LA; Yazdabadi, A; Moore, J; Russell, J; Smith, P; Chandra, RV; Brooks, M; Jhamb, A; Chong, WS; Maingard, J; Asadi, H				Nasra, Mohamed; Jaffri, Rimsha; Pavlin-Premrl, Davor; Kok, Hong Kuan; Khabaza, Ali; Barras, Christen; Slater, Lee-Anne; Yazdabadi, Anousha; Moore, Justin; Russell, Jeremy; Smith, Paul; Chandra, Ronil V.; Brooks, Mark; Jhamb, Ashu; Chong, Winston; Maingard, Julian; Asadi, Hamed			Can artificial intelligence improve patient educational material readability? A systematic review and narrative synthesis	INTERNAL MEDICINE JOURNAL												Enhancing patient comprehension of their health is crucial in improving health outcomes. The integration of artificial intelligence (AI) in distilling medical information into a conversational, legible format can potentially enhance health literacy. This review aims to examine the accuracy, reliability, comprehensiveness and readability of medical patient education materials (PEMs) simplified by AI models. A systematic review was conducted searching for articles assessing outcomes of use of AI in simplifying PEMs. Inclusion criteria are as follows: publication between January 2019 and June 2023, various modalities of AI, English language, AI use in PEMs and including physicians and/or patients. An inductive thematic approach was utilised to code for unifying topics which were qualitatively analysed. Twenty studies were included, and seven themes were identified (reproducibility, accessibility and ease of use, emotional support and user satisfaction, readability, data security, accuracy and reliability and comprehensiveness). AI effectively simplified PEMs, with reproducibility rates up to 90.7% in specific domains. User satisfaction exceeded 85% in AI-generated materials. AI models showed promising readability improvements, with ChatGPT achieving 100% post-simplification readability scores. AI's performance in accuracy and reliability was mixed, with occasional lack of comprehensiveness and inaccuracies, particularly when addressing complex medical topics. AI models accurately simplified basic tasks but lacked soft skills and personalisation. These limitations can be addressed with higher-calibre models combined with prompt engineering. In conclusion, the literature reveals a scope for AI to enhance patient health literacy through medical PEMs. Further refinement is needed to improve AI's accuracy and reliability, especially when simplifying complex medical information.					Slater, Lee-Anne/AAC-4151-2022; Chandra, Ronil/GPK-0357-2022	Nasra, Mohamed/0000-0002-0818-8285; 													1444-0903	1445-5994				JAN	2025	55	1					20	34		10.1111/imj.16607	http://dx.doi.org/10.1111/imj.16607		DEC 2024						39720869					WOS:001382481600001
J	Deroncele-Acosta, A; Sayán-Rivera, RME; Mendoza-López, AD; Norabuena-Figueroa, ED				Deroncele-Acosta, Angel; Sayan-Rivera, Rosa Maria Elizabeth; Mendoza-Lopez, Angel Deciderio; Norabuena-Figueroa, Emerson Damian			Generative Artificial Intelligence and Transversal Competencies in Higher Education: A Systematic Review	APPLIED SYSTEM INNOVATION												Generative AI is an emerging tool in higher education; however, its connection with transversal competencies, as well as their sustainable adoption, remains underexplored. The study aims to analyze the scientific and conceptual development of generative artificial intelligence in higher education to identify the most relevant transversal competencies, strategic processes for its sustainable implementation, and global trends in academic production. A systematic literature review (PRISMA) was conducted on the Web of Science, Scopus, and PubMed, analyzing 35 studies for narrative synthesis and 897 publications for bibliometric analysis. The transversal competencies identified were: Academic Integrity, Critical Thinking, Innovation, Ethics, Creativity, Communication, Collaboration, AI Literacy, Responsibility, Digital Literacy, AI Ethics, Autonomous Learning, Self-Regulation, Flexibility, and Leadership. The conceptual framework connotes the interdisciplinary nature and five key processes were identified to achieve the sustainable integration of Generative AI in higher education oriented to the development of transversal competencies: (1) critical and ethical appropriation, (2) institutional management of technological infrastructure, (3) faculty development, (4) curricular transformation, and (5) pedagogical innovation. On bibliometric behavior, scientific articles predominate, with few systematic reviews. China leads in publication volume, and social sciences are the most prominent area. It is concluded that generative artificial intelligence is key to the development of transversal competencies if it is adopted from a critical, ethical, and pedagogically intentional approach. Its implications and future projections in the field of higher education are discussed.					Deroncele-Acosta, Angel/IRZ-1383-2023															2571-5577				JUN 18	2025	8	3							83	10.3390/asi8030083	http://dx.doi.org/10.3390/asi8030083													WOS:001515253800001
J	Watkins, R; Barak-Medina, E				Watkins, Ryan; Barak-Medina, Eran			AI's Influence on Human Creative Agency	CREATIVITY RESEARCH JOURNAL												Emerging Artificial Intelligence (AI) capabilities are redefining roles traditionally assigned to humans or tools in numerous tasks, and thereby creating tensions in professions ranging from education and engineering, to design and film. As a result, we suggest now is the time for greater vitality in a professional dialogue and research agenda on how AI, especially Generative AI, affects human creative agency. While AI poses challenges to human creative agency, it also offers growth potential, demanding a balanced approach to its opportunities and risks. To bolster a professional dialogue, we propose a framework detailing three key attributes of AI's impact on creative agency: whether AI is perceived as a competitor or a complement to human skills; AI's perceived effectiveness and performance; and, whether the AI systems perform a high-stakes or a low-stakes function. We then propose AI literacy as a moderating influence on these attributes. Our aims for this framework are to (i) serve as a starting point for developing research-based strategies and tools that will allow AI to augment human creative agency, rather than diminish it, and (ii) provide a useful foundation for conversations between creativity researchers and AI developers.																			1040-0419	1532-6934				2024 DEC 6	2024										10.1080/10400419.2024.2437264	http://dx.doi.org/10.1080/10400419.2024.2437264		DEC 2024											WOS:001370561400001
J	Quarshie, B; Poku, KM				Quarshie, Benjamin; Poku, Kelcy Menkah			Dynamic resonance: unpacking Ghanaian traditional knowledge through proverbs for modern socio-environmental innovation	FRONTIERS IN HUMAN DYNAMICS												Traditional knowledge reflects the essence of a community, embodying its truths and ancestral lineage. Preserving this knowledge is vital for maintaining identity and cultural roots. However, viewing it as the sole marker of ethnic ancestry overlooks other factors, such as genetics and the interplay of beliefs and practises. Beliefs and practises, shaped by cumulative wisdom, represent a dynamic core of traditional knowledge influenced by geography, experiences, cultural encounters, and resource availability. Tradition is not static but evolves with time, adapting to the needs of the era. Thus, it is essential to critically evaluate traditional knowledge within its temporal context to distinguish sustainable practises from those that may hinder progress. This paper examines select traditional knowledge embedded in proverbs from two Ghanaian ethnic cultures, Akan and Ewe, through the lens of 21st-century sustainable practises. The focus is to demonstrate that whilst some traditional knowledge endures, others align with modern skills like creativity, innovation, critical thinking, and collaboration-key to socio-environmental sustainability. The paper begins by appreciating Ghanaian traditional knowledge and its practical applications in daily life. It then presents a selection of proverbs with their interpretations, followed by a critical review guided by 21st-century benchmarks with the aid of ChatGPT 4.0 and Gemini 1.5 pro language modelling Artificial Intelligence (AIs) after authentication of the selected proverbs by language experts who are also vested in Ghanaian proverbs. The analysis highlights the nuanced fabric of traditional knowledge, identifying some proverbs that remain relevant and adaptable for daily usage in educational and industrial organisations to elicit 21st-century competencies. The paper concludes with recommendations for scholarly contributions and educational initiatives grounded in traditional knowledge. These initiatives aim to foster sustainable, innovative practises that meet contemporary needs, bridging cultural heritage and modernity.					Quarshie, Benjamin/JHT-9291-2023															2673-2726				FEB 20	2025	7								1456870	10.3389/fhumd.2025.1456870	http://dx.doi.org/10.3389/fhumd.2025.1456870													WOS:001437765800001
J	Batista, J; Mesquita, A; Carnaz, G				Batista, Joao; Mesquita, Anabela; Carnaz, Goncalo			Generative AI and Higher Education: Trends, Challenges, and Future Directions from a Systematic Literature Review	INFORMATION												(1) Background: The development of generative artificial intelligence (GAI) is transforming higher education. This systematic literature review synthesizes recent empirical studies on the use of GAI, focusing on its impact on teaching, learning, and institutional practices. (2) Methods: Following PRISMA guidelines, a comprehensive search strategy was employed to locate scientific articles on GAI in higher education published by Scopus and Web of Science between January 2023 and January 2024. (3) Results: The search identified 102 articles, with 37 meeting the inclusion criteria. These studies were grouped into three themes: the application of GAI technologies, stakeholder acceptance and perceptions, and specific use situations. (4) Discussion: Key findings include GAI's versatility and potential use, student acceptance, and educational enhancement. However, challenges such as assessment practices, institutional strategies, and risks to academic integrity were also noted. (5) Conclusions: The findings help identify potential directions for future research, including assessment integrity and pedagogical strategies, ethical considerations and policy development, the impact on teaching and learning processes, the perceptions of students and instructors, technological advancements, and the preparation of future skills and workforce readiness. The study has certain limitations, particularly due to the short time frame and the search criteria, which might have varied if conducted by different researchers.					; Carnaz, Gonçalo/GXF-8763-2022; Batista, Joao/AAG-1859-2019; Mesquita, Anabela/B-3353-2008	Batista, Joao/0000-0002-5872-5341; 														2078-2489				NOV	2024	15	11							676	10.3390/info15110676	http://dx.doi.org/10.3390/info15110676													WOS:001365458100001