Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References

Athaluri, Sai Anirudh; Manthena, Sandeep Varma; Kesapragada, V S R Krishna Manoj; Yarlagadda, Vineel; Dave, Tirth; Duddumpudi, Rama Tulasi Siri

doi:10.7759/cureus.37432

Cited by 108 publications

(66 citation statements)

References 5 publications

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“…This suggests that whilst ChatGPT can identify patterns and organize data, it has limitations in fully understanding the underlying meaning and context of the information (Sinha et al, 2023). To mitigate the problems of generating 'hallucinatory' or fabricated responses (Athaluri et al, 2023;Masters, 2023;Thirunavukarasu et al, 2023), this study took a cautious and specific approach to eliciting answers about endodontics. This was achieved using a prompt that clearly indicated the desired type of answer: 'Only yes or no answer'.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers

Suárez,

Díaz‐Flores García,

Algar

et al. 2023

Int Endodontic J

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AimChatbot Generative Pre‐trained Transformer (ChatGPT) is a generative artificial intelligence (AI) software based on large language models (LLMs), designed to simulate human conversations and generate novel content based on the training data it has been exposed to. The aim of this study was to evaluate the consistency and accuracy of ChatGPT‐generated answers to clinical questions in endodontics, compared to answers provided by human experts.MethodologyNinety‐one dichotomous (yes/no) questions were designed and categorized into three levels of difficulty. Twenty questions were randomly selected from each difficulty level. Sixty answers were generated by ChatGPT for each question. Two endodontic experts independently answered the 60 questions. Statistical analysis was performed using the SPSS program to calculate the consistency and accuracy of the answers generated by ChatGPT compared to the experts. Confidence intervals (95%) and standard deviations were used to estimate variability.ResultsThe answers generated by ChatGPT showed high consistency (85.44%). No significant differences in consistency were found based on question difficulty. In terms of answer accuracy, ChatGPT achieved an average accuracy of 57.33%. However, significant differences in accuracy were observed based on question difficulty, with lower accuracy for easier questions.ConclusionsCurrently, ChatGPT is not capable of replacing dentists in clinical decision‐making. As ChatGPT's performance improves through deep learning, it is expected to become more useful and effective in the field of endodontics. However, careful attention and ongoing evaluation are needed to ensure its accuracy, reliability and safety in endodontics.

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Section: Discussionmentioning

confidence: 99%

Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers

Suárez,

Díaz‐Flores García,

Algar

et al. 2023

Int Endodontic J

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“…Then, there were questions that prompted rating the appropriateness of LLM use for 23 different tasks in clinical practice, research and education on a 5-point Likert scale (i.e., highly appropriate to highly inappropriate). Those tasks represented a sample of proposed LLM uses that were synthesized form the literature and included, but were not limited to, optimizing alerts for clinical decision support, providing a differential diagnosis, writing a discharge summary, recommending treatment options, translating radiology reports into layperson language, writing scientific manuscripts, and generating personalized study plans for students or trainees among others 2,6,7,8,[15][16][17][18][19][20][21][22][23][24][25] .…”

Section: Study Design and Samplingmentioning

confidence: 99%

A Survey of Clinicians' Views of the Utility of Large Language Models

Spotnitz,

Idnay,

Gordon

et al. 2024

Appl Clin Inform

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Objective: Large language models (LLMs) like ChatGPT are powerful algorithms that have been shown to produce human-like text from input data. A number of potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed healthcare providers for their opinions about whether the technology is fit for use. Materials and Methods: We distributed a validated mixed-methods survey to gauge practicing clinicians’ comfort with LLMs for a breadth of tasks in clinical practice, research and education, which were selected from the literature. Results: A total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, healthcare providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias. Discussion: Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee. Conclusion: In a uniquely rigorous and comprehensive mixed-methods survey of clinicians about LLM use, healthcare providers were encouraging of having LLMs in healthcare for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence (AI) in general.

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“…The phenomenon of AI hallucination, wherein an LLM generates false information based on patterns that do not exist in the source material, provides evidence of this problem. Why AI tools create content that is false or misleading is not fully understood and reflects an underlying degree of uncertainty (Athaluri et al, 2023).…”

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confidence: 99%

Artificial intelligence and the Journal of Research in Science Teaching

Sadler,

Mensah,

Tam

2024

J Res Sci Teach

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Artificial Intelligence (AI) is a transformative technology that promises to impact many aspects of society including research, education, and publishing. We, the editors of the Journal of Research in Science Teaching (JRST), think that the journal has a responsibility to contribute to the ongoing dialogues about the use of AI in research and publishing with particular attention to the field of science education. We use this editorial to share our current ideas about the opportunities and challenges associated with AI in science education research and to sketch out new journal guidelines related to the use of AI for the production of JRST articles. We also extend an invitation to scholars to submit research articles and commentaries that advance the field's understanding of the intersections of AI and science education.Establishing foundations for an AI revolution has been in progress since the mid-twentieth century (Adamopoulou & Moussiades, 2020), and a giant step in public engagement with AI was taken in November 2022 when OpenAI released ChatGPT. This tool along with other large language models (LLM) such as Google Bard, and Microsoft's Copilot, provide platforms that are easy to use and can generate content such as text, images, computer code, audio, and video. It has quickly become apparent that these generative AI tools have the potential to change education in substantial ways. There is already evidence that students and teachers are actively using AI in ways that will push the field of education to reconsider what it means to construct learning artifacts, how to assess the work of learners, and the nature of learner-technology interactions (e.g., Prather et al., 2023). Of course, generative AI will not just impact the work of students, teachers, and other educational practitioners, it will affect how research is conducted and reported. As journal editors, we are particularly interested in the use of AI in the sharing of research and publication processes.Across the field of education research, and science education research more specifically, scholars use a host of technologies to support their work. For example, researchers regularly use statistical packages to derive quantitative patterns in data, qualitative software to organize and represent coded themes in data, grammar, and spelling check software embedded in word processors and online (i.e., Grammarly), and reference managers to find and cite literature. Technologies such as these examples are ubiquitous across our field, and new generative AI presents another set of tools that researchers might leverage for the sharing of their scholarship. However, the now widely available LLMs seem, to us, to represent a fundamental shift in technological capacity for producing research publications. The users of software for data analysis, reference management, and grammar checks exert levels of control and supervision over these technologies, which is not the case when using an LLM. There is a much greater degree of opaqueness and uncertainty when it comes to...

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Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References

Cited by 108 publications

References 5 publications

Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers

Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers

A Survey of Clinicians' Views of the Utility of Large Language Models

Artificial intelligence and the Journal of Research in Science Teaching

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