Efficacy and risks of artificial intelligence chatbots for anxiety and depression: a narrative review of recent clinical studies.

This research article unpacks the perceptions of the academic community on the adoption of artificial intelligence (AI) chatbots (chatting robots) by tertiary students in Zimbabwean universities. The article seeks to understand the usage of AI chatbots in education, their opportunities, challenges, concerns and prospects of using AI chatbots in educational settings. The research findings revolve around the perceptions and scepticism of the adoption of AI chatbots in education, as seen from students, lecturers and librarians developing higherorder cognitive skills. The main objectives were to identify the main AI chatbots commonly used by tertiary students, to explore the opportunities of adopting of AI chatbots to students and to expose the pitfalls associated with the usage of AI by tertiary students. Participants were drawn from tertiary students, lecturers and university library staff members. The study employed qualitative methodologies, including in-depth interviews, observational checklist and focus groups. The findings suggest that AI chatbot is both a curse and a blessing to tertiary students. The study reveals that AI chatbots enhance learning experience, enable them to overcome skill gaps, bring insights on assignment writing and aid in exam preparation. The study reveals that AI chatbots foster the development of higher-order cognitive skills by augmenting traditional lectures, test preparation and personalisation. However, pitfalls include plagiarism, outdated information, shallow information, indolent and slothful laziness in students, as well as financial constraints associated with AI chatbots. The study recommends that universities must invest in workshops to train staff and students on the responsible ways of adopting and using AI to reduce the increase of luddites. Universities are recommended to develop referencing systems allowing students to acknowledge using AI chatbots as sources. Tertiary students are also recommended to fuse AI with human capacity, desisting from the culture of relying solely on AI chatbots.

Purpose This paper aims to examine the relationships between anthropomorphic cues (i.e. degrees of the humanized profile picture and naming) in artificial intelligence (AI) chatbots and business types (utilitarian-centered business vs hedonic-centered business) on consumers’ attitudes toward the AI chatbot and intentions to use the AI chatbot app and to accept the AI chatbot’s recommendation. Design/methodology/approach An online experiment with a 2 (humanized profile pictures: low [semihumanoid] vs high [full-humanoid]) × 2 (naming: Mary vs virtual assistant) × 2 (business types: utilitarian-centered business [bank] vs hedonic-centered business [café]) between-subjects design (N = 520 Mturk samples) was used. Findings The results of this study show significant main effects of anthropomorphic cues (i.e. degrees of profile picture and naming) in AI chatbots and three-way interactions among humanized profile pictures, naming and business types (utilitarian-centered business vs hedonic-centered business) on consumers’ attitudes toward the AI chatbot, intentions to use the AI chatbot app and intentions to accept the AI chatbot’s recommendation. This indicates that the high level of anthropomorphism generates more positive attitudes toward the AI chatbot and intentions to use the AI chatbot app and to accept the AI chatbot’s recommendation in the hedonic-centered business condition. Moreover, the mediated role of parasocial interaction occurs in this relationship. Originality/value This study is the original endeavor to examine the moderating role of business types influencing the effect of anthropomorphism on consumers’ responses, while existing literature overweighted the value of anthropomorphism in AI chatbots without considering the variation of businesses.

Background: AI chatbots are predominantly trained on English contents. They perform well in answering English cancer questions, but their performance in other languages (such as Spanish) is unknown. Spanish-speaking patients are also concerned that they must use the paywall versions to get better responses, which may exacerbate existing cancer disparities. Methods: We evaluated the responses of AI chatbots to most searched Spanish cancer questions. Using Google Trends (1/1/2020-1/1/2024), we identified the top 5 most searched Spanish cancer questions related to the top 3 common cancers in US Hispanics/Latinos. We selected 6 popular AI chatbots (free and paywall versions of ChatGPT, Claude, and Gemini) and then generated 90 Spanish responses. Board-certified oncologists speaking native Spanish assessed the quality using DISCERN Instrument (score from 1 [low quality] to 5 [high quality]), actionability using Patient Education Materials Assessment Tool (score from 0 [no clear action suggestions] to 100% [clear action suggestions]), readability using Fernández Huerta Reading Grade Level (score from 1 [1st grade] to 13 [college]). Results: The quality of overall AI chatbot responses was moderate (mean [95% CI]: 3.5 [3.4-3.6]). The actionability was low (mean [95% CI]: 35.6% [30.8%-40.3%]), and the readability was high-school level (mean [95% CI]: 9.2 [8.8-9.6] grade). The performance of quality, actionability, and readability did not differ by free and paywall versions (P >0.05). Conclusions: AI chatbots provided moderately accurate information for most searched Spanish cancer-related questions. The responses were not readily actionable and written at the high-school level, which was not concordant with the American Medical Association’s recommendation (6th grade or lower). The performance did not improve by using the paywall versions. Relevance: To reduce cancer disparities in health literacy, AI chatbots need improvement in responding to Spanish cancer questions. Citation Format: En Cheng, Jesus D. Anampa, Carolina Bernabe-Ramirez, Juan Lin, Xiaonan Xue, Alyson B. Moadel-Robblee, Edward Chu. Artificial intelligence (AI) chatbots and their reponses to most searched Spanish cancer questions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4909.

Category:Sports; TraumaIntroduction/Purpose:Artificial intelligence (AI) chatbots have recently gained popularity as a source of information that can be easily accessed by patients given their human-like responses to prompts and questions. Within orthopaedics, the treatment of acute Achilles tendon ruptures is not uniform due to varying surgical repair techniques, postoperative protocols, and nonoperative treatment options dependent on surgeon preference and patient factors. Given that patients are increasingly turning toward AI for questions about medical diagnoses and treatment options, our study looked to compare the adequacy of AI chatbot responses to frequently asked questions regarding acute Achilles tendon ruptures.Methods:Three popular AI platforms (ChatGPT, Google Gemini, and Microsoft Bing AI) were prompted for a concise response to ten commonly asked questions regarding Achilles tendon rupture management (Table 1). Four board-certified subspecialty-trained orthopaedic surgeons (two in foot and ankle, two in sports medicine) were asked to assess the value of the AI response using a four-point scale (1 – satisfactory; 2 – satisfactory requiring minimal clarification; 3 – satisfactory requiring substantial clarification; 4 – unsatisfactory). A Kruskal-Wallis test was used to compare the responses between the three AI platforms using the scores assigned by the surgeons.Results:All three AI chatbots provided comparable answers to 7 of 10 questions (70%). Of all the responses (30 total), only two (6.7%) had a mean rating of 3 or higher. Significant differences were noted between the AI systems for questions 4 [H(2) = 7.258, p = .027], 7 [H(2) = 6.308, p = .043], and 10 [H(2) = 6.796, p = .033]. Post hoc analyses revealed Bing AI had significantly worse scores as compared to ChatGPT for all three of these questions.Conclusion:AI chatbots can appropriately answer concise prompts about the diagnosis and management of acute Achilles tendon ruptures often sought out by patients prior to or after evaluation by an orthopaedic surgeon. The responses provided by the three AI chatbots analyzed in our study were uniform and satisfactory, with only one of the platforms scoring worse on three of the ten questions. As AI chatbots advance, they will become a valuable tool for patient education in orthopaedics. Future studies will be needed to assess performance as new AI chatbots develop and large language models continue to evolve.Table 1: List of 10 selected frequently asked questions regarding acute Achilles tendon ruptures

Purpose Many companies invest in artificial intelligence (AI) chatbots to create new-age interactive platforms for consumers to achieve business goals. The research on AI chatbots from a marketing perspective is scant and scattered across the sectors. This paper aims to analyse extant research on AI chatbots in the marketing context, providing insights on leading work, journals, institutions, authors, trends and future research directions. Design/methodology/approach This study used the Scopus database to identify 242 articles published between 1996 and 2023 on AI chatbots in the area of business management and decision sciences. This bibliometric analysis used VOS viewer software to analyse the publication and citation structure, co-authorship, collaboration network of institutions and countries, keyword co-occurrence and bibliographic coupling. Findings The study provides valuable insights from the most cited articles, shedding light on their contribution to AI chatbot research in the marketing area. It also highlighted the publication trends, notable authors, journals and bibliographic analyses to identify key trends in AI chatbot-oriented marketing. The result reveals that consumer-oriented chatbot research is presently focused on understanding consumer perception of chatbots. Consumer chatbot experience and engagement are future research areas for AI chatbots in the marketing domain. The bibliometric analysis unveils that research on AI chatbot role in marketing is currently in nascent stages and there is limited intellectual exchange to understand the consumer intention toward chatbot use. Research limitations/implications This study not only provides a comprehensive overview of AI chatbot research in marketing during the past 27 years but also suggests future opportunities for researchers to work on AI chatbots in a marketing context. To further enhance the comprehensiveness of data collection, it is recommended to include another source like the Web of Science, which is among the largest research databases. Originality/value The research contributes significantly to the study of the extant research on AI chatbots in marketing from the Scopus database for the period from 1996 to 2023. This is probably the most comprehensive bibliometric analysis conducted to understand the status of research on AI chatbots and identify trends and future research directions. This research helps in coordinating intellectual networks among institutions, authors and countries.

Artificial intelligence (AI) chatbots are gaining increasing popularity in education. Due to their increasing popularity, many empirical studies have been devoted to exploring the effects of AI chatbots on students' learning outcomes. The proliferation of experimental studies has highlighted the need to summarize and synthesize the inconsistent findings about the effects of AI chatbots on students' learning outcomes. However, few reviews focused on the meta‐analysis of the effects of AI chatbots on students' learning outcomes. The present study performed a meta‐analysis of 24 randomized studies utilizing Stata software (version 14). The main goal of the current study was to meta‐analytically examine the effects of AI chatbots on students' learning outcomes and the moderating effects of educational levels and intervention duration. The results indicated that AI chatbots had a large effect on students' learning outcomes. Moreover, AI chatbots had a greater effect on students in higher education, compared to those in primary education and secondary education. In addition, short interventions were found to have a stronger effect on students' learning outcomes than long interventions. It could be explained by the argument that the novelty effects of AI chatbots could improve learning outcomes in short interventions, but it has worn off in the long interventions. Future designers and educators should make attempt to increase students' learning outcomes by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligence. Practitioner notes What is already known about this topic In recent years, artificial intelligence (AI) chatbots have been gaining increasing popularity in education. Studies undertaken so far have provided conflicting evidence concerning the effects of AI chatbots on students' learning outcomes. There has remained a paucity of meta‐analyses synthesizing the contradictory findings about the effects of AI chatbots on students' learning outcomes. What this paper adds This study, through meta‐analysis, synthesized these recent findings about the effects of AI chatbots on students' learning outcomes. This study found that AI chatbots could have a large effect on students' learning outcomes. This study found that the effects of AI chatbots were moderated by educational levels and intervention duration. Implications for practice and/or policy AI chatbot designers could make AI chatbots better by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligence Practitioners and/or teachers should draw attention to the positive and negative effects of AI chatbots on students. Considering the importance of ChatGPT, more research is required to develop a better understanding of the effects of ChatGPT in education. More research is needed to examine the mechanisms underlying the effects of AI chatbots on students' learning outcomes.

This study investigated how the use of a conversational artificial intelligence (AI) chatbot improved medical students' patient-centered communication (PCC) skills and how it affected their motivation to learn using innovative interactive tools such as AI chatbots throughout their careers. This study adopted a onegroup post-test-only design to investigate the impact of AI chatbot-based learning on medical students' PCC skills, their learning motivation with AI chatbots, and their perception towards the use of AI chatbots in their learning. After a series of classroom activities, including metaverse exploration, AI chatbot-based learning activities, and classroom discussions, 43 medical students completed three surveys that measured their motivation to learn using AI tools for medical education, their perception towards the use of AI chatbots in their learning, and their self-assessment of their PCC skills. Our findings revealed significant correlations among learning motivation, PCC scores, and perception variables. Notably, the perception towards AI chatbot-based learning and AI chatbot learning motivation showed a very strong positive correlation (r=0.72), indicating that motivated students were more likely to perceive chatbots as beneficial educational tools. Additionally, a moderate correlation between motivation and self-assessed PCC skills (r=0.54) indicated that students motivated to use AI chatbots tended to rate their PCC skills more favorably. Similarly, a positive relationship (r=0.68) between students' perceptions of chatbot usage and their self-assessed PCC skills indicated that enhancing students' perceptions of AI tools could lead to better educational outcomes.

Background/Objectives: Antimicrobial resistance represents a growing global health crisis, demanding innovative approaches to improve antibiotic stewardship. Artificial intelligence (AI) chatbots based on large language models have shown potential as tools to support clinicians, especially non-specialists, in optimizing antibiotic therapy. This review aims to synthesize current evidence on the capabilities, limitations, and future directions for AI chatbots in enhancing antibiotic selection and patient outcomes. Methods: A narrative review was conducted by analyzing studies published in the last five years across databases such as PubMed, SCOPUS, Web of Science, and Google Scholar. The review focused on research discussing AI-based chatbots, antibiotic stewardship, and clinical decision support systems. Studies were evaluated for methodological soundness and significance, and the findings were synthesized narratively. Results: Current evidence highlights the ability of AI chatbots to assist in guideline-based antibiotic recommendations, improve medical education, and enhance clinical decision-making. Promising results include satisfactory accuracy in preliminary diagnostic and prescriptive tasks. However, challenges such as inconsistent handling of clinical nuances, susceptibility to unsafe advice, algorithmic biases, data privacy concerns, and limited clinical validation underscore the importance of human oversight and refinement. Conclusions: AI chatbots have the potential to complement antibiotic stewardship efforts by promoting appropriate antibiotic use and improving patient outcomes. Realizing this potential will require rigorous clinical trials, interdisciplinary collaboration, regulatory clarity, and tailored algorithmic improvements to ensure their safe and effective integration into clinical practice.

Objective: The study aims to compare the ability of free artificial intelligence (AI) chatbots to detect drug interactions with freely available drug interaction tools, using clopidogrel as an example. Material and Method: The Lexicomp database was used as a reference to determine drug interactions with clopidogrel. ChatGPT-3.5 AI and Bing AI were selected as the free AI chatbots. Medscape Drug Interaction Checker, DrugBank Drug Interaction Checker and Epocrates Interaction Check were selected as free drug interaction tools. Accuracy score and comprehensiveness score were calculated for each drug interaction tool and AI chatbots. The kappa coefficient was calculated to assess inter-source agreement for interaction severity. Result and Discussion: The results most similar to those of Lexicomp were obtained from the DrugBank and the ChatGPT-3.5 AI chatbot. The ChatGPT-3.5 AI chatbot performed best, with 69 correct results and an accuracy score of 307. ChatGPT-3.5 AI has the highest overall score of 387 points for accuracy and comprehensiveness. In addition, the highest kappa coefficient with Lexicomp was found for ChatGPT-3.5 AI chatbot (0.201, fair agreement). However, some of the results obtained by ChatGPT-3.5 AI need to be improved as they are incorrect/inadequate. Therefore, information obtained using AI tools should not be used as a reference for clinical applications by healthcare professionals and patients should not change their treatment without consulting doctor.

To evaluate and compare the validity and reliability of different artificial intelligence (AI) chatbots in answering queries about potential orthodontic risks. Answers to 20 frequently asked questions about the potential risks of orthodontics were derived from daily consultations with experienced orthodontists and AI chatbots (ChatGPT 4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro). The questions were repeated three times and submitted to the AI chatbots to assess the reliability of their answers. The answers from AI chatbots were scored using a modified Global Quality Scale (GQS). Low- and high-threshold validity tests were used to determine validity, and Cronbach's alpha was used to evaluate the consistency of the three responses to each of the 20 questions. In the low-threshold validity test, Gemini exhibited the highest overall performance. In the high-threshold validity test, Gemini also showed the highest overall effectiveness, but there was no significant difference observed among the three chatbots. All three chatbots demonstrated satisfactory levels of reliability, with Gemini having the highest consistency. AI chatbots have some potential in providing orthodontic risk information, but they must be used cautiously and further optimized to improve their effectiveness in clinical practice.

Effectiveness of artificial intelligence (AI) chatbots in providing labor epidural analgesia information: are we there yet?

Purpose This study aims to investigate the key factors influencing students’ adoption of artificial intelligence (AI) chatbot applications in higher education. It further examines the mediating and moderating role of AI chatbots and tech readiness in determining the effect of perceived usefulness, subjective norms, tech simplicity and tech literacy on the intention to use AI chatbot applications. Design/methodology/approach A survey was conducted at Malaysian universities whereby 430 students participated and 426 responses were deemed valid for analysis. The data was carefully examined to ensure the accuracy of the proposed model. To comprehend the intricate relationships in the model, the authors used the partial least squares structural equation modeling (PLS-SEM) technique. Findings The results revealed that perceived usefulness (PU), subjective norm (SN), tech literacy (TL) and tech simplicity (TS) have a significant impact on students’ intention to use AI applications. The intention to use AI chatbot applications significantly influences the adoption of AI chatbots in higher education. The findings indicated that the intention to use AI chatbots mediates the effect of PU, SN, TL and TS on the adoption of AI chatbots. Tech readiness (TR) moderates the effect of PU and TS on the intention to use AI chatbot applications. Originality/value This research addresses a new insight into AI chatbot adoption within higher education, particularly demonstrating how AI chatbots and tech readiness act as mediators and moderators in shaping students’ perceptions and adoption of AI chatbot applications.

This editorial discusses the role of artificial intelligence (AI) chatbots in the healthcare sector, emphasizing their potential as supplements rather than substitutes for medical professionals. While AI chatbots have demonstrated significant potential in managing routine tasks, processing vast amounts of data, and aiding in patient education, they still lack the empathy, intuition, and experience intrinsic to human healthcare providers. Furthermore, the deployment of AI in medicine brings forth ethical and legal considerations that require robust regulatory measures. As we move towards the future, the editorial underscores the importance of a collaborative model, wherein AI chatbots and medical professionals work together to optimize patient outcomes. Despite the potential for AI advancements, the likelihood of chatbots completely replacing medical professionals remains low, as the complexity of healthcare necessitates human involvement. The ultimate aim should be to use technology like AI chatbots to enhance patient care and outcomes, not to replace the irreplaceable human elements of healthcare.

This study investigated the instructional effects of learner uptake following automatic corrective recast from artificial intelligence (AI) chatbots on the learning of the English caused-motion construction. 69 novice-level EFL learners in a Korean high school were recruited to investigate the instructional effects of corrective recast from AI chatbots on the learning of the English caused-motion construction. Results from the elicited writing tasks (EWT) revealed that statistically significant gains were observed in both immediate and delayed posttests for the production of the English caused-motion construction by experimental group participants. Also, the relationship between learner uptake from AI chatbots’ corrective recast and the learning of the English caused-motion construction were analyzed. The results demonstrated that learners’ successful repair from AI chatbots’ corrective recast was positively correlated with the learning gains in the two EWT posttests. The study concludes by highlighting the significance of noticeability in AI chatbots’ corrective feedback for foreign language learning.

IntroductionThis study explores the effects of Artificial Intelligence (AI) chatbots, with a particular focus on OpenAI’s ChatGPT, on Higher Education Institutions (HEIs). With the rapid advancement of AI, understanding its implications in the educational sector becomes paramount.MethodsUtilizing databases like PubMed, IEEE Xplore, and Google Scholar, we systematically searched for literature on AI chatbots’ impact on HEIs. Our criteria prioritized peer-reviewed articles, prominent media outlets, and English publications, excluding tangential AI chatbot mentions. After selection, data extraction focused on authors, study design, and primary findings. The analysis combined descriptive and thematic approaches, emphasizing patterns and applications of AI chatbots in HEIs.ResultsThe literature review revealed diverse perspectives on ChatGPT’s potential in education. Notable benefits include research support, automated grading, and enhanced human-computer interaction. However, concerns such as online testing security, plagiarism, and broader societal and economic impacts like job displacement, the digital literacy gap, and AI-induced anxiety were identified. The study also underscored the transformative architecture of ChatGPT and its versatile applications in the educational sector. Furthermore, potential advantages like streamlined enrollment, improved student services, teaching enhancements, research aid, and increased student retention were highlighted. Conversely, risks such as privacy breaches, misuse, bias, misinformation, decreased human interaction, and accessibility issues were identified.DiscussionWhile AI’s global expansion is undeniable, there is a pressing need for balanced regulation in its application within HEIs. Faculty members are encouraged to utilize AI tools like ChatGPT proactively and ethically to mitigate risks, especially academic fraud. Despite the study’s limitations, including an incomplete representation of AI’s overall effect on education and the absence of concrete integration guidelines, it is evident that AI technologies like ChatGPT present both significant benefits and risks. The study advocates for a thoughtful and responsible integration of such technologies within HEIs.