How much can we rely on artificial intelligence chatbots such as the ChatGPT software program to assist with scientific writing?

Dashti, Mahmood; Londono, Jimmy; Ghasemi, Shohreh; Moghaddasi, Negar

doi:10.1016/j.prosdent.2023.05.023

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…The synergistic utilization of artificial intelligence and FEA studies holds the potential to unlock valuable insights into the practical applications of dental implants. [37][38][39][40][41] Further long-term clinical studies to examine the effectiveness of NRC are needed.…”

Section: Discussionmentioning

confidence: 99%

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Hashemi,

Baghaei,

Fathi

et al. 2023

Eur J Dent

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Objectives Using finite element analysis (FEA), this study aimed to determine the effect of nonrigid connectors (NRCs) and their position on the success of tooth and implant-supported fixed prostheses in the maxillary posterior region. Materials and Methods Three three-dimensional FEA models were designed, presuming maxillary second premolar and first molar to be extracted. Implant (replacing first molar), abutment, bone (spongious and cortical), first premolar (containing dentin, root cement, gutta-percha, and casting post and core), periodontal ligament, and three three-unit cemented porcelain-fused-to-metal prostheses (a rigid one and two nonrigid) were modeled. The NRC was once on the tooth side and once on the implant side. The prostheses were loaded twice. The first molar (180 N) and premolars (120 N) teeth were subjected to progressive vertical and oblique (12-degree) loads, and maximum von Mises stress and strain in teeth and connectors were calculated for each model. Results The findings of the current study showed evidence that tooth-implant design with an NRC has significantly increased the average stress in the tooth. The average stress in dentin was 769.02 for the mesial connector and 766.95 for the distal connector, and this was only 731.59 for rigid connector. Furthermore, it was observed that rigid connector has considerably minimized the stress within the tooth–implant-supported fixed partial denture. The average stress for the crown and metal frame is 346.22 and 526.41 in rigid connector, while it is 1,172.9 and 2,050.9 for the nonrigid mesial connector. Conclusion Although distal NRC was more efficient than mesial NRC, using NRC will only reduce the stress applied to cortical bone and is not recommended in the posterior region of the maxilla.

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Section: Discussionmentioning

confidence: 99%

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Hashemi,

Baghaei,

Fathi

et al. 2023

Eur J Dent

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“…This inability to access and verify the sources provided by ChatGPT poses significant obstacles to maintaining academic integrity and conducting rigorous scholarly research. [24].…”

Section: Copying and Plagiarismmentioning

confidence: 99%

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies (Preprint)

Xu,

Weng,

Liu

et al. 2024

Preprint

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UNSTRUCTURED ChatGPT, a Generative Pre-trained Transformer, has garnered global attention and sparked discussions since its introduction in early 2023. However, it has stirred controversy within the realms of medical education and scientific research. This paper focuses on exploring the potentials, challenges, and corresponding strategies associated with ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant utility in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nevertheless, a myriad of challenges accompany these advancements. In the realm of education, it is imperative to prevent excessive reliance on ChatGPT and combat academic plagiarism. Similarly, in the medical domain, ensuring the timeliness, accuracy, and credibility of ChatGPT-generated content is crucial. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies. Firstly, mitigate the risk of over-reliance on ChatGPT and academic plagiarism through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. Embracing modern educational approaches alongside ChatGPT enhances the overall quality of medical education. Enhance the professionalism and reliability of the generated content by implementing measures such as optimizing ChatGPT's training data professionally and enhancing the transparency of the generation process. This ensures that the generated content aligns with the most recent standards of medical practice. Furthermore, enhancing value alignment and establishing relevant laws or codes of practice address ethical concerns, including algorithmic discrimination, medical responsibility allocation, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

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“…The issue of the inaccuracy of the information provided and analyzed by Artificial Intelligence (AI) has been present since the beginning of its usage in scientific writing. This applies to commercially available AI tools up to date [26]. Therefore, authors and reviewers should doublecheck the relevance of the information presented by AI.…”

Section: Introductionmentioning

confidence: 99%

<strong>Generative Artificial Intelligence, AI for Scientific Writing: A Literature Review</strong>

Shopovski

2024

Preprint

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The growing usage of Generative AI tools in scientific writing requires a critical examination of their benefits and challenges. This literature review is aimed at comprehensively analyzing current empirical research articles focused on the application of Generative AI in scientific writing. The Google Scholar database was used to search for the literature. The following keywords were used: "Generative AI" and "academic writing", "LLM" (Large Language Models) and "academic writing", "Generative AI" and "Scientific writing", and "ChatGPT" and "Scientific Writing". The search was restricted to articles published between January 1, 2023, and April 30, 2024. 15 articles were selected as appropriate for the study and analyzed. It was found that, thus far, ChatGPT is the most exploited tool in the studies. AI tools such as Bard (Gemini), Bing, Claude2, and Elicit were also tested. The benefits of Generative AI usage in scientific writing were found to be omnipresent. It can aid in the generation of structured abstracts, titles, introductions, literature reviews, and conclusions of a scientific article. Generative AI also makes writing more efficient and time-saving. Its capabilities in improving language and proofreading are well-established. However, the generation of inaccurate content and references by current commercially available LLMs poses a serious problem. The lack of critical thinking and tendency to produce non-original content are significant drawbacks. Generative AI should be employed with human oversight, serving as an assistant rather than a replacement. Transparency in AI usage in scientific writing is essential, along with the necessity for proper legal regulation.

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How much can we rely on artificial intelligence chatbots such as the ChatGPT software program to assist with scientific writing?

Cited by 15 publications

References 9 publications

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies (Preprint)

<strong>Generative Artificial Intelligence, AI for Scientific Writing: A Literature Review</strong>

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