Exploring artificial intelligence appraisal: Appraisal patterns in GPT-generated and human-authored book reviews

Getting AI Right: Introductory Notes on AI & Society

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI’s potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI’s efficiencies in editorial processes.

Artificial intelligence: Friend or foe?

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

Deep Learning and Lung Cancer: AI to Extract Information Hidden in Routine CT Scans.

Accessible generative artificial intelligence (AI) tools like large-language models (LLMs) (e.g., Chat-GPT, 1 Minerva 2 ) are raising a flurry of questions about the potential and implications of generative algorithms and the ethical use of AI-generated text in a variety of contexts, including open science (Bugbee & Ramachandran, 2023), student assessment (Heidt, 2023), and medicine (Harrer, 2023) . Similarly, among the graphic and visual arts communities, the use of generative image synthesis algorithms (e.g., DALL-E, 3 Midjourney, 4 Stable Diffusion 5 ) that take text prompts as input and produce works in the style of a particular human artist, or no artist who ever lived, are causing consternation and posing challenging questions (Murphy, 2022; Plunkett, 2022) . The use of generative AI to create deep fakes has also been in the spotlight (Ruiter, 2021), as has its role in answering scientific research questions directly (Castelvecchi, 2023) . To our minds, the questions these technologies are raising do not seem to be of a fundamentally different character to questions asked about AI for many years. They largely concern (a) what is possible, (b) what is right, and (c) the implications of the technology's use. For instance,

Dear Editors, I have read your editorials with great interest [1,2]. I am interested in sharing my insights concerning the role of artificial intelligence in composing scholarly articles, along with its potential as a collaborative co-author. I extend my heartfelt gratitude for establishing this profoundly valuable platform for discussion. I am aware of the imperative to renew myself academically daily. Perhaps the most exquisite yet arduous facet of academic life resides herein. Sustaining the currency of my domain knowledge, tracking technological advancements, and aligning with the latest research trends often pose formidable challenges. However, these challenges also furnish avenues for continuous self-improvement and exploring topics demanding more profound comprehension. In addition to the facilitation of information access afforded by computers and the internet, artificial intelligence has been incorporated in recent years—my inaugural encounter with artificial intelligence manifested through applications utilized on telecommunication devices. Artificial intelligence finds application across various domains and displays a swiftly burgeoning spectrum of applications. In recent years, significant advancements have transpired in artificial intelligence, culminating in the emergence of numerous Large Language Models (LLMs). Introducing sophisticated and diverse language models has precipitated a remarkable leap in this domain. One such model is the artificial intelligence conversational robot named ChatGPT, equipped with the GPT-3.5.5 language model, which OpenAI unveiled on November 30, 2022. Impressively, this model garnered one million users within five days. Within the academic literature, ChatGPT, a Chat Generative-Being Transformer, is widely acknowledged as a substantial and versatile information resource [3]. So, can ChatGPT be used safely for manuscript writing? As academics, we know that writing an article and adding new knowledge to the literature requires serious dedication. In this context, using ChatGPT for article writing involves significant risks [4]. The biggest problem is accuracy [5]. Artificial intelligence draws its data from the internet environment, where the veracity and reliability of information are persistently subject to debate. The accuracy and reliability of data on the Internet is always controversial. ChatGPT can produce factually inaccurate and inaccurate texts, create biased texts, and in particular, this can undermine the credibility and authority of researchers. Another most critical problem is that it includes ethical concerns. However, we cannot overlook the fact that with the advancement of technology, artificial intelligence has been progressing toward the core of our lives. As a solution, I think that artificial intelligence should be employed with caution, considering its ethical problems, the potential for misapplications, and plagiarism-related concerns. Notably, it can contribute to refining written text rather than printing the entire article. In addition, as you stated, the role, contributions, and process of ChatGPT in the article should be clearly stated. In the literature, it has been said that ChatGPT contributed to various stages, such as data analysis, model development, and interpretation of results [6]. Susnjak [7] has argued that ChatGPT exhibits critical thinking skills and can generate highly realistic texts with minimal input, positing that this poses a threat in online examinations, particularly within higher education settings. Zhai [8] in the context of crafting articles encompassing education and artificial intelligence themes, has emphasized the assertion that ChatGPT could assist researchers in generating coherent, partially accurate, informative, and systematic articles. Alshater [9] has noted that ChatGPT has the potential to improve academic performance, underlined various limitations, such as ethical considerations, and emphasized the importance of combining human analysis and interpretation. So, is it appropriate for ChatGPT to be credited as a co-author? This topic will always be controversial This matter will inevitably remain subject to ongoing debate. The scope of ChatGPT's contribution and the ethical considerations surrounding this practice, coupled with the continued discussions within the academic community, suggest that employing ChatGPT as a co-author carries substantial risks [10]. In a collaborative study where Perlman and ChatGPT served as co-authors [11], Perlman evaluated the text generated by ChatGPT and underscored the possibility of envisioning a new future by considering the ethical concerns, faulty applications, and plagiarism issues associated with artificial intelligence. Similarly, in a comparable endeavor, Srivastava [12] was tasked with using ChatGPT to compose a conference paper and consequently emphasized that, under researcher supervision, ChatGPT could be an efficient application. In conclusion, the assertion that artificial intelligence plays an increasingly significant role in research and scientific discovery is continuously gaining support. However, considering the meticulousness and accuracy required for establishing academic literature across all fields, ChatGPT's practice of generating academic articles from scratch and serving as an assistant author is not aligned with academic norms. There is a need for the development of more nuanced programs in this regard. Especially in the coming days, ChatGPT should prove the information it gives and present the right references for every sentence. Simultaneously, ChatGPT should be revamped in a format that can address ethical concerns. Yours sincerely,

Are AI language models such as ChatGPT ready to improve the care of individuals with epilepsy?

Artificial intelligence (AI) has advanced substantially in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fraudulent papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers. The aim of this study was to investigate the capabilities of current AI language models in generating high-quality fraudulent medical articles. We hypothesized that modern AI models can create highly convincing fraudulent papers that can easily deceive readers and even experienced researchers. This proof-of-concept study used ChatGPT (Chat Generative Pre-trained Transformer) powered by the GPT-3 (Generative Pre-trained Transformer 3) language model to generate a fraudulent scientific article related to neurosurgery. GPT-3 is a large language model developed by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts given by users. The model was trained on a massive corpus of text from the internet and is capable of generating high-quality text in a variety of languages and on various topics. The authors posed questions and prompts to the model and refined them iteratively as the model generated the responses. The goal was to create a completely fabricated article including the abstract, introduction, material and methods, discussion, references, charts, etc. Once the article was generated, it was reviewed for accuracy and coherence by experts in the fields of neurosurgery, psychiatry, and statistics and compared to existing similar articles. The study found that the AI language model can create a highly convincing fraudulent article that resembled a genuine scientific paper in terms of word usage, sentence structure, and overall composition. The AI-generated article included standard sections such as introduction, material and methods, results, and discussion, as well a data sheet. It consisted of 1992 words and 17 citations, and the whole process of article creation took approximately 1 hour without any special training of the human user. However, there were some concerns and specific mistakes identified in the generated article, specifically in the references. The study demonstrates the potential of current AI language models to generate completely fabricated scientific articles. Although the papers look sophisticated and seemingly flawless, expert readers may identify semantic inaccuracies and errors upon closer inspection. We highlight the need for increased vigilance and better detection methods to combat the potential misuse of AI in scientific research. At the same time, it is important to recognize the potential benefits of using AI language models in genuine scientific writing and research, such as manuscript preparation and language editing.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Clinical efficacy of AI in lung SABR planning: A comparative retrospective analysis.

Large Language Models (LLMs) have been adopted increasingly in oncology, for example, in structuring data from clinical notes, inferring diagnoses from free text or imaging data, and anonymizing of data. Due to the rapid development pace of LLMs, best practices for conducting and reporting oncological research in these applications have yet to be fully established.We queried PubMed for oncology-related LLM research with the last cutoff set at Dec 31st 2024. We investigated 179 papers. Of these, 131 were removed due to omission criteria, and 48 were structured and reported here. Inclusion criteria were oncology-related research and full research articles. Structured fields included date of submission, acceptance, and publishing, the granularity of model reporting (model family, model snapshot), reporting of key LLM model parameters, availability of source code and data, and programming language and API details. We noted an almost exponential growth of LLM-related publications in oncology, with a relatively short time from authors’ submission to publicly available publication (median 3.7 months, IQR 2.5-5.9 months). Interestingly, despite the relatively short processing time, in 25% of cases, the exact model essential to the publication had been deprecated by the model service providers or a newer version was available at the time of publishing. 35.4% of published research relied solely on a graphical user-interface (GUI) of LLMs such as ChatGPT, while 37.5% reported programmatically API-use, with Python as the most common language. While most publications either fully or partially reported the utilized prompts (75%), only 22.9% reported the exact key model parameters, such as temperature. Even when the temperature parameter was available, 45.4% of these publications used a temperature value larger than 0, resulting in more stochastic answers. Source code was made publicly available in 18.7% of publications that reported using a programming language such as Python or R. While practically all publications (97.9%) reported the used model families such as GPT-4o, Claude 3.5 Sonnet or Llama 3-70B, only 27% reported the exact model snapshot usage such as GPT-4o with snapshot options available for May 13th, August 6th or November 20th in 2024. We exemplify and report shortcomings of recent LLM adoption in oncological research. To alleviate these issues, we propose a checklist to improve reproducibility, transparency, and longevity of LLM research directed at researchers and journals. We propose the following preliminary checklist: exact reporting of model snapshot and model parameter bound to a specific snapshot instead of latest release, API usage instead of GUI chatbots, temperature-parameter equal to 0, assessment of variability across runs, session restarts to avoid biases, and caution in researching models that are bound to be deprecated due to the short turn-around time in LLMs. Additionally, rigorous prompt engineering and especially few-shot learning show potential in optimizing interactions with LLMs, also in oncology. Citation Format: Tolou Shadbahr, Antti S. Rannikko, Tuomas Mirtti, Teemu D. Laajala. Current oncological large language model research lacks reproducibility, transparency, and long term support [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B021.

Harnessing artificial intelligence in bariatric surgery: comparative analysis of ChatGPT-4, Bing, and Bard in generating clinician-level bariatric surgery recommendations