Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review

Personal experience with AI-generated peer reviews: a case study

BACKGROUND

While some recent studies have looked at large language model (LLM) use in peer review at the corpus level, to date there have been few examinations of instances of AI-generated reviews in their social context. The goal of this first-person account is to present my experience of receiving two anonymous peer review reports that I believe were produced using generative AI, as well as lessons learned from that experience.

METHODS

This is a case report on the timeline of the incident, and my and the journal's actions following it. Supporting evidence includes text patterns in the reports, online AI detection tools and ChatGPT simulations; recommendations are offered for others who may find themselves in a similar situation. The primary research limitation of this article is that it is based on one individual's personal experience.

RESULTS

After alleging the use of generative AI in December 2023, two months of back-and-forth ensued between myself and the journal, leading to my withdrawal of the submission. The journal denied any ethical breach, without taking an explicit position on the allegations of LLM use. Based on this experience, I recommend that authors engage in dialogue with journals on AI use in peer review prior to article submission; where undisclosed AI use is suspected, authors should proactively amass evidence, request an investigation protocol, escalate the matter as needed, involve independent bodies where possible, and share their experience with fellow researchers.

CONCLUSIONS

Journals need to promptly adopt transparent policies on LLM use in peer review, in particular requiring disclosure. Open peer review where identities of all stakeholders are declared might safeguard against LLM misuse, but accountability in the AI era is needed from all parties.

The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool

Using artificial intelligence (AI) in research offers many important benefits for science and society but also creates novel and complex ethical issues. While these ethical issues do not necessitate changing established ethical norms of science, they require the scientific community to develop new guidance for the appropriate use of AI. In this article, we briefly introduce AI and explain how it can be used in research, examine some of the ethical issues raised when using it, and offer nine recommendations for responsible use, including: (1) Researchers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors; (2) Researchers should disclose, describe, and explain their use of AI in research, including its limitations, in language that can be understood by non-experts; (3) Researchers should engage with impacted communities, populations, and other stakeholders concerning the use of AI in research to obtain their advice and assistance and address their interests and concerns, such as issues related to bias; (4) Researchers who use synthetic data should (a) indicate which parts of the data are synthetic; (b) clearly label the synthetic data; (c) describe how the data were generated; and (d) explain how and why the data were used; (5) AI systems should not be named as authors, inventors, or copyright holders but their contributions to research should be disclosed and described; (6) Education and mentoring in responsible conduct of research should include discussion of ethical use of AI.

Generative Artificial Intelligence in Surgical Publishing

This Viewpoint discusses the role of generative artificial intelligence in surgical publishing, including idea generation, study conduct, manuscript preparation, and manuscript review.

From statistics to deep learning: Using large language models in psychiatric research

BACKGROUND

Large Language Models (LLMs) hold promise in enhancing psychiatric research efficiency. However, concerns related to bias, computational demands, data privacy, and the reliability of LLM-generated content pose challenges. GAP: Existing studies primarily focus on the clinical applications of LLMs, with limited exploration of their potentials in broader psychiatric research.

OBJECTIVE

This study adopts a narrative review format to assess the utility of LLMs in psychiatric research, beyond clinical settings, focusing on their effectiveness in literature review, study design, subject selection, statistical modeling, and academic writing.

IMPLICATION

This study provides a clearer understanding of how LLMs can be effectively integrated in the psychiatric research process, offering guidance on mitigating the associated risks and maximizing their potential benefits. While LLMs hold promise for advancing psychiatric research, careful oversight, rigorous validation, and adherence to ethical standards are crucial to mitigating risks such as bias, data privacy concerns, and reliability issues, thereby ensuring their effective and responsible use in improving psychiatric research.

Large Language Models Applied to Health Care Tasks May Improve Clinical Efficiency, Value of Care Rendered, Research, and Medical Education

Large language models (LLMs) are generative artificial intelligence models that create content on the basis of the data on which it was trained. Processing capabilities have evolved from text only to being multimodal including text, images, audio, and video features. In health care settings, LLMs are being applied to several clinically important areas, including patient care and workflow efficiency, communications, hospital operations and data management, medical education, practice management, and health care research. Under the umbrella of patient care, several core use cases of LLMs include simplifying documentation tasks, enhancing patient communication (interactive language and written), conveying medical knowledge, and performing medical triage and diagnosis. However, LLMs warrant scrutiny when applied to health care tasks, as errors may have negative implications for health care outcomes, specifically in the context of perpetuating bias, ethical considerations, and cost-effectiveness. Customized LLMs developed for more narrow purposes may help overcome certain performance limitations, transparency challenges, and biases present in contemporary generalized LLMs by curating training data. Methods of customizing LLMs broadly fall under 4 categories: prompt engineering, retrieval augmented generation, fine-tuning, and agentic augmentation, with each approach conferring different information-retrieval properties for the LLM. LEVEL OF EVIDENCE: Level V, expert opinion.

Artificial Intelligence in Peer Review: Enhancing Efficiency While Preserving Integrity

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.

Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals

BACKGROUND

Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of recommendations of GAI usage guidelines among medical journals and how these factors relate to journal characteristics.

METHODS

From the SCImago Journal Rank (SJR) list for medicine in 2022, we generated two groups of journals: top SJR ranked journals (N = 200) and random sample of non-top SJR ranked journals (N = 140). For each group, we examined the coverage of author and reviewer guidelines across four categories: no guidelines, external guidelines only, own guidelines only, and own and external guidelines. We then calculated the number of recommendations by counting the number of usage recommendations for author and reviewer guidelines separately. Regression models examined the relationship of journal characteristics with the coverage and type of recommendations of GAI usage guidelines.

RESULTS

A higher proportion of top SJR ranked journals provided author guidelines compared to the random sample of non-top SJR ranked journals (95.0% vs. 86.7%, P < 0.01). The two groups of journals had the same median of 5 on a scale of 0 to 7 for author guidelines and a median of 1 on a scale of 0 to 2 for reviewer guidelines. However, both groups had lower percentages of journals providing recommendations for data analysis and interpretation, with the random sample of non-top SJR ranked journals having a significantly lower percentage (32.5% vs. 16.7%, P < 0.05). A higher SJR score was positively associated with providing GAI usage guidelines for both authors (all P < 0.01) and reviewers (all P < 0.01) among the random sample of non-top SJR ranked journals.

CONCLUSIONS

Although most medical journals provided their own GAI usage guidelines or referenced external guidelines, some recommendations remained unspecified (e.g., whether AI can be used for data analysis and interpretation). Additionally, journals with lower SJR scores were less likely to provide guidelines, indicating a potential gap that warrants attention. Collaborative efforts are needed to develop specific recommendations that better guide authors and reviewers.

Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities

Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools and platforms for collaboration and sharing results. Due to this relationship, the characteristics of the employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) is increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, and data curation/analysis. Nevertheless, concerns about openness, transparency, and bias suggest that GenAI may benefit from greater engagement with OS. GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values, such as fairness, transparency, and integrity, and may harm various social actors. In this paper, we explore the possible positive and negative impacts of GenAI on OS. We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems. However, due to GenAI's limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research. Hence, sufficient checks, validation, and critical assessments are essential when incorporating GenAI into research workflows.

The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

Use of large language models as artificial intelligence tools in academic research and publishing among global clinical researchers

With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information. Our study provides a snapshot of global researchers' perception of current trends and future impacts of LLMs in research. Using a cross-sectional design, we surveyed 226 medical and paramedical researchers from 59 countries across 65 specialties, trained in the Global Clinical Scholars' Research Training certificate program of Harvard Medical School between 2020 and 2024. Majority (57.5%) of these participants practiced in an academic setting with a median of 7 (2,18) PubMed Indexed published articles. 198 respondents (87.6%) were aware of LLMs and those who were aware had higher number of publications (p < 0.001). 18.7% of the respondents who were aware (n = 37) had previously used LLMs in publications especially for grammatical errors and formatting (64.9%); however, most (40.5%) did not acknowledge its use in their papers. 50.8% of aware respondents (n = 95) predicted an overall positive future impact of LLMs while 32.6% were unsure of its scope. 52% of aware respondents (n = 102) believed that LLMs would have a major impact in areas such as grammatical errors and formatting (66.3%), revision and editing (57.2%), writing (57.2%) and literature review (54.2%). 58.1% of aware respondents were opined that journals should allow for use of AI in research and 78.3% believed that regulations should be put in place to avoid its abuse. Seeing the perception of researchers towards LLMs and the significant association between awareness of LLMs and number of published works, we emphasize the importance of developing comprehensive guidelines and ethical framework to govern the use of AI in academic research and address the current challenges.

Use of Artificial Intelligence in Peer Review Among Top 100 Medical Journals

This cross-sectional study of 100 top medical journals examines policies for use of artificial intelligence (AI) and generative AI in peer review.

Artificial intelligence, ChatGPT, and dental education: Implications for reflective assignments and qualitative research

INTRODUCTION

Reflections enable students to gain additional value from a given experience. The use of Chat Generative Pre-training Transformer (ChatGPT, OpenAI Incorporated) has gained momentum, but its impact on dental education is understudied.

OBJECTIVES

To assess whether or not university instructors can differentiate reflections generated by ChatGPT from those generated by students, and to assess whether or not the content of a thematic analysis generated by ChatGPT differs from that generated by qualitative researchers on the same reflections.

METHODS

Hardcopies of 20 reflections (10 generated by undergraduate dental students and 10 generated by ChatGPT) were distributed to three instructors who had at least 5 years of teaching experience. Instructors were asked to assign either 'ChatGPT' or 'student' to each reflection. Ten of these reflections (five generated by undergraduate dental students and five generated by ChatGPT) were randomly selected and distributed to two qualitative researchers who were asked to perform a brief thematic analysis with codes and themes. The same ten reflections were also thematically analyzed by ChatGPT.

RESULTS

The three instructors correctly determined whether the reflections were student or ChatGPT generated 85% of the time. Most disagreements (40%) happened with the reflections generated by ChatGPT, as the instructors thought to be generated by students. The thematic analyses did not differ substantially when comparing the codes and themes produced by the two researchers with those generated by ChatGPT.

CONCLUSIONS

Instructors could differentiate between reflections generated by ChatGPT or by students most of the time. The overall content of a thematic analysis generated by the artificial intelligence program ChatGPT did not differ from that generated by qualitative researchers. Overall, the promising applications of ChatGPT will likely generate a paradigm shift in (dental) health education, research, and practice.

Large language models can help boost food production, but be mindful of their risks

Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In this short perspective, we examine risks and opportunities related to more widespread adoption of language models in food production systems. While LLMs can potentially enhance agricultural efficiency, drive innovation, and inform better policies, challenges like agricultural misinformation, collection of vast amounts of farmer data, and threats to agricultural jobs are important concerns. The rapid evolution of the LLM landscape underscores the need for agricultural policymakers to think carefully about frameworks and guidelines that ensure the responsible use of LLMs in food production before these technologies become so ingrained that policy intervention becomes challenging.

Status of peer review guidelines in international surgical journals: A cross‐sectional survey

Aim

To gain insight into the current status of peer review guidelines in international surgical journals and to offer guidance for the development of peer review guidelines for surgical journals.

Methods

We selected the top 100 journals in the category of ‘Surgery’ according to the Journal Citation Report 2021. We conducted a search of the websites of these journals, and Web of Science, PubMed, other databases, in order to gather the peer review guidelines published by these top 100 journals up until June 30, 2022. Additionally, we analysed the contents of these peer review guidelines.

Results

Only 52% (52/100) of journals provided guidelines for reviewers. Sixteen peer review guidelines which were published by these 52 surgical journals were included in this study. The contents of these peer review guidelines were classified into 33 items. The most common item was research methodology, which was mentioned by 13 journals (25%, 13/52). Other important items include statistical methodology, mentioned by 11 journals (21.2%), the rationality of figures, tables, and data, mentioned by 11 journals (21.2%), innovation of research, mentioned by nine journals (17.3%), and language expression, readability of papers, ethical review, references, and so forth, mentioned by eight journals (15.4%). Two journals described items for quality assessment of peer review. Forty‐three journals offered a checklist to guide reviewers on how to write a review report. Some surgical journals developed peer review guidelines for reviewers with different academic levels, such as professional reviewers and patient/public reviewers. Additionally, some surgical journals provided specific items for different types of papers, such as original articles, reviews, surgical videos, surgical database research, surgery‐related outcome measurements, and case reports in their peer review guidelines.

Conclusions

Key contents of peer review guidelines for the reviewers of surgical journals not only include items relating to reviewing research methodology, statistical methods, figures, tables and data, research innovation, ethical review, but also cover items concerning reviewing surgical videos, surgical database research, surgery‐related outcome measurements, instructions on how to write a review report, and guidelines on how to assess quality of peer review.

The transformative impact of large language models on medical writing and publishing: current applications, challenges and future directions

Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

Evolution of Research Reporting Standards: Adapting to the Influence of Artificial Intelligence, Statistics Software, and Writing Tools

Reporting standards are essential to health research as they improve accuracy and transparency. Over time, significant changes have occurred to the requirements for reporting research to ensure comprehensive and transparent reporting across a range of study domains and foster methodological rigor. The establishment of the Declaration of Helsinki, Consolidated Standards of Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) are just a few of the historic initiatives that have increased research transparency. Through enhanced discoverability, statistical analysis facilitation, article quality enhancement, and language barrier reduction, artificial intelligence (AI)-in particular, large language models like ChatGPT-has transformed academic writing. However, problems with errors that could occur and the need for transparency while utilizing AI tools still exist. Modifying reporting rules to include AI-driven writing tools such as ChatGPT is ethically and practically challenging. In academic writing, precautions for truth, privacy, and responsibility are necessary due to concerns about biases, openness, data limits, and potential legal ramifications. The CONSORT-AI and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-AI Steering Group expands the CONSORT guidelines for AI clinical trials-new checklists like METRICS and CLEAR help to promote transparency in AI studies. Responsible usage of technology in research and writing software adoption requires interdisciplinary collaboration and ethical assessment. This study explores the impact of AI technologies, specifically ChatGPT, on past reporting standards and the need for revised guidelines for open, reproducible, and robust scientific publications.

A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research

OBJECTIVES

The emergence of large language models has resulted in a significant shift in informatics research and carries promise in clinical cancer care. Here we provide a narrative review of the recent use of large language models (LLMs) to support cancer care, prevention, and research.

METHODS

We performed a search of the Scopus database for studies on the application of bidirectional encoder representations from transformers (BERT) and generative-pretrained transformer (GPT) LLMs in cancer care published between the start of 2021 and the end of 2023. We present salient and impactful papers related to each of these themes.

RESULTS

Studies identified focused on aspects of clinical decision support (CDS), cancer education, and support for research activities. The use of LLMs for CDS primarily focused on aspects of treatment and screening planning, treatment response, and the management of adverse events. Studies using LLMs for cancer education typically focused on question-answering, assessing cancer myths and misconceptions, and text summarization and simplification. Finally, studies using LLMs to support research activities focused on scientific writing and idea generation, cohort identification and extraction, clinical data processing, and NLP-centric tasks.

CONCLUSIONS

The application of LLMs in cancer care has shown promise across a variety of diverse use cases. Future research should utilize quantitative metrics, qualitative insights, and user insights in the development and evaluation of LLM-based cancer care tools. The development of open-source LLMs for use in cancer care research and activities should also be a priority.

Addressing emerging issues in entomology: 2023 student debates

The Entomological Society of America (ESA) Student Debates is an annual student competition at the ESA Annual Meeting organized by Student Debates Subcommittee (SDS) members of the ESA Student Affairs Committee. In conjunction with the 2023 ESA Annual Meeting theme, 'Insects and influence: Advancing entomology's impact on people and policy', the theme of this year's student debate was 'Addressing emerging issues in entomology'. With the aid of ESA membership, the SDS selected the following debate topics: (1) Should disclosure of artificial intelligence large language models in scientific writing always be required? and (2) Is it more important to prioritize honey bee or native pollinator health for long-term food security within North America? Four student teams from across the nation, composed of 3-5 student members and a professional advisor, were assigned a topic and stance. Over the course of 5 months, all team members researched and prepared for their assigned topic before debating live with an opposing team at the 2023 ESA Annual Meeting in National Harbor, Maryland. SDS members additionally prepared and presented introductions for each debate topic to provide unbiased backgrounds to the judges and audience for context in assessing teams' arguments. The result was an engaging discussion between our teams, judges, and audience members on emerging issues facing entomology and its impact on people and policy, such as scientific communication and food security, that brought attention to the complexities involved when debating topics concerning insects and influence.

An AI assistant to help review and improve causal reasoning in epidemiological documents

Drawing sound causal inferences from observational data is often challenging for both authors and reviewers. This paper discusses the design and application of an Artificial Intelligence Causal Research Assistant (AIA) that seeks to help authors improve causal inferences and conclusions drawn from epidemiological data in health risk assessments. The AIA-assisted review process provides structured reviews and recommendations for improving the causal reasoning, analyses and interpretations made in scientific papers based on epidemiological data. Causal analysis methodologies range from earlier Bradford-Hill considerations to current causal directed acyclic graph (DAG) and related models. AIA seeks to make these methods more accessible and useful to researchers. AIA uses an external script (a "Causal AI Booster" (CAB) program based on classical AI concepts of slot-filling in frames organized into task hierarchies to complete goals) to guide Large Language Models (LLMs), such as OpenAI's ChatGPT or Google's LaMDA (Bard), to systematically review manuscripts and create both (a) recommendations for what to do to improve analyses and reporting; and (b) explanations and support for the recommendations. Review tables and summaries are completed systematically by the LLM in order. For example, recommendations for how to state and caveat causal conclusions in the Abstract and Discussion sections reflect previous analyses of the Study Design and Data Analysis sections. This work illustrates how current AI can contribute to reviewing and providing constructive feedback on research documents. We believe that such AI-assisted review shows promise for enhancing the quality of causal reasoning and exposition in epidemiological studies. It suggests the potential for effective human-AI collaboration in scientific authoring and review processes.

Evaluation of Large Language Model Performance and Reliability for Citations and References in Scholarly Writing: Cross-Disciplinary Study

BACKGROUND

Large language models (LLMs) have gained prominence since the release of ChatGPT in late 2022.

OBJECTIVE

The aim of this study was to assess the accuracy of citations and references generated by ChatGPT (GPT-3.5) in two distinct academic domains: the natural sciences and humanities.

METHODS

Two researchers independently prompted ChatGPT to write an introduction section for a manuscript and include citations; they then evaluated the accuracy of the citations and Digital Object Identifiers (DOIs). Results were compared between the two disciplines.

RESULTS

Ten topics were included, including 5 in the natural sciences and 5 in the humanities. A total of 102 citations were generated, with 55 in the natural sciences and 47 in the humanities. Among these, 40 citations (72.7%) in the natural sciences and 36 citations (76.6%) in the humanities were confirmed to exist (P=.42). There were significant disparities found in DOI presence in the natural sciences (39/55, 70.9%) and the humanities (18/47, 38.3%), along with significant differences in accuracy between the two disciplines (18/55, 32.7% vs 4/47, 8.5%). DOI hallucination was more prevalent in the humanities (42/55, 89.4%). The Levenshtein distance was significantly higher in the humanities than in the natural sciences, reflecting the lower DOI accuracy.

CONCLUSIONS

ChatGPT's performance in generating citations and references varies across disciplines. Differences in DOI standards and disciplinary nuances contribute to performance variations. Researchers should consider the strengths and limitations of artificial intelligence writing tools with respect to citation accuracy. The use of domain-specific models may enhance accuracy.

ChatGPT in healthcare: A taxonomy and systematic review

The recent release of ChatGPT, a chat bot research project/product of natural language processing (NLP) by OpenAI, stirs up a sensation among both the general public and medical professionals, amassing a phenomenally large user base in a short time. This is a typical example of the 'productization' of cutting-edge technologies, which allows the general public without a technical background to gain firsthand experience in artificial intelligence (AI), similar to the AI hype created by AlphaGo (DeepMind Technologies, UK) and self-driving cars (Google, Tesla, etc.). However, it is crucial, especially for healthcare researchers, to remain prudent amidst the hype. This work provides a systematic review of existing publications on the use of ChatGPT in healthcare, elucidating the 'status quo' of ChatGPT in medical applications, for general readers, healthcare professionals as well as NLP scientists. The large biomedical literature database PubMed is used to retrieve published works on this topic using the keyword 'ChatGPT'. An inclusion criterion and a taxonomy are further proposed to filter the search results and categorize the selected publications, respectively. It is found through the review that the current release of ChatGPT has achieved only moderate or 'passing' performance in a variety of tests, and is unreliable for actual clinical deployment, since it is not intended for clinical applications by design. We conclude that specialized NLP models trained on (bio)medical datasets still represent the right direction to pursue for critical clinical applications.

A guide for social science journal editors on easing into open science

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org ) has collated several resources on embedding open science in journal editing ( www.dpjedi.org/resources ). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx ).

Using AI Tools in Writing Peer Review Reports: Should Academic Journals Embrace the Use of ChatGPT?

This letter highlights a pressing issue regarding the absence of established editorial policies for the utilization of AI tools (e.g., ChatGPT) in the peer review process. The increasing adoption of AI tools in academic publishing necessitates the formulation of standardized guidelines to ensure fairness, transparency, and accountability. Without clear editorial policies, there is a threat of compromising the integrity of the peer review process and undermining the credibility of academic publications. Urgent attention is needed to address this gap and establish robust protocols that govern the use of AI tools in peer review.

Affiliation Bias in Peer Review of Abstracts by a Large Language Model

This study assesses affiliation bias in peer review of medical abstracts by a commonly used large language model.

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.

A SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of ChatGPT in the Medical Literature: Concise Review

BACKGROUND

ChatGPT is a 175-billion-parameter natural language processing model that is already involved in scientific content and publications. Its influence ranges from providing quick access to information on medical topics, assisting in generating medical and scientific articles and papers, performing medical data analyses, and even interpreting complex data sets.

OBJECTIVE

The future role of ChatGPT remains uncertain and a matter of debate already shortly after its release. This review aimed to analyze the role of ChatGPT in the medical literature during the first 3 months after its release.

METHODS

We performed a concise review of literature published in PubMed from December 1, 2022, to March 31, 2023. To find all publications related to ChatGPT or considering ChatGPT, the search term was kept simple ("ChatGPT" in AllFields). All publications available as full text in German or English were included. All accessible publications were evaluated according to specifications by the author team (eg, impact factor, publication modus, article type, publication speed, and type of ChatGPT integration or content). The conclusions of the articles were used for later SWOT (strengths, weaknesses, opportunities, and threats) analysis. All data were analyzed on a descriptive basis.

RESULTS

Of 178 studies in total, 160 met the inclusion criteria and were evaluated. The average impact factor was 4.423 (range 0-96.216), and the average publication speed was 16 (range 0-83) days. Among the articles, there were 77 editorials (48,1%), 43 essays (26.9%), 21 studies (13.1%), 6 reviews (3.8%), 6 case reports (3.8%), 6 news (3.8%), and 1 meta-analysis (0.6%). Of those, 54.4% (n=87) were published as open access, with 5% (n=8) provided on preprint servers. Over 400 quotes with information on strengths, weaknesses, opportunities, and threats were detected. By far, most (n=142, 34.8%) were related to weaknesses. ChatGPT excels in its ability to express ideas clearly and formulate general contexts comprehensibly. It performs so well that even experts in the field have difficulty identifying abstracts generated by ChatGPT. However, the time-limited scope and the need for corrections by experts were mentioned as weaknesses and threats of ChatGPT. Opportunities include assistance in formulating medical issues for nonnative English speakers, as well as the possibility of timely participation in the development of such artificial intelligence tools since it is in its early stages and can therefore still be influenced.

CONCLUSIONS

Artificial intelligence tools such as ChatGPT are already part of the medical publishing landscape. Despite their apparent opportunities, policies and guidelines must be implemented to ensure benefits in education, clinical practice, and research and protect against threats such as scientific misconduct, plagiarism, and inaccuracy.

Be or Not to Be With ChatGPT?

In the ever-evolving realm of scientific research, this letter underscores the vital role of ChatGPT as an invaluable ally in manuscript creation, focusing on its remarkable grammar and spelling error correction capabilities. Furthermore, it highlights ChatGPT's efficacy in expediting the manuscript preparation process by streamlining the collection and highlighting critical scientific information. By elucidating the aim of this letter and the multifaceted benefits of ChatGPT, we aspire to illuminate the path toward a future where scientific writing achieves unparalleled efficiency and precision.

An exploratory survey about using ChatGPT in education, healthcare, and research

OBJECTIVE

ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare.

MATERIALS AND METHODS

We surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with a Fisher's Exact. Furthermore, we used qualitative methods to analyze and code discussions.

RESULTS

We received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare.

DISCUSSION

There was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.

Open Science 2.0: Towards a truly collaborative research ecosystem

Conversations about open science have reached the mainstream, yet many open science practices such as data sharing remain uncommon. Our efforts towards openness therefore need to increase in scale and aim for a more ambitious target. We need an ecosystem not only where research outputs are openly shared but also in which transparency permeates the research process from the start and lends itself to more rigorous and collaborative research. To support this vision, this Essay provides an overview of a selection of open science initiatives from the past 2 decades, focusing on methods transparency, scholarly communication, team science, and research culture, and speculates about what the future of open science could look like. It then draws on these examples to provide recommendations for how funders, institutions, journals, regulators, and other stakeholders can create an environment that is ripe for improvement.

The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts

In this article, we discuss ethical issues related to using and disclosing artificial intelligence (AI) tools, such as ChatGPT and other systems based on large language models (LLMs), to write or edit scholarly manuscripts. Some journals, such as Science, have banned the use of LLMs because of the ethical problems they raise concerning responsible authorship. We argue that this is not a reasonable response to the moral conundrums created by the use of LLMs because bans are unenforceable and would encourage undisclosed use of LLMs. Furthermore, LLMs can be useful in writing, reviewing and editing text, and promote equity in science. Others have argued that LLMs should be mentioned in the acknowledgments since they do not meet all the authorship criteria. We argue that naming LLMs as authors or mentioning them in the acknowledgments are both inappropriate forms of recognition because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do. Tools in general, and software in particular, are usually cited in-text, followed by being mentioned in the references. We provide suggestions to improve APA Style for referencing ChatGPT to specifically indicate the contributor who used LLMs (because interactions are stored on personal user accounts), the used version and model (because the same version could use different language models and generate dissimilar responses, e.g., ChatGPT May 12 Version GPT3.5 or GPT4), and the time of usage (because LLMs evolve fast and generate dissimilar responses over time). We recommend that researchers who use LLMs: (1) disclose their use in the introduction or methods section to transparently describe details such as used prompts and note which parts of the text are affected, (2) use in-text citations and references (to recognize their used applications and improve findability and indexing), and (3) record and submit their relevant interactions with LLMs as supplementary material or appendices.

The Potential and Concerns of Using AI in Scientific Research: ChatGPT Performance Evaluation

BACKGROUND

Artificial intelligence (AI) has many applications in various aspects of our daily life, including health, criminal, education, civil, business, and liability law. One aspect of AI that has gained significant attention is natural language processing (NLP), which refers to the ability of computers to understand and generate human language.

OBJECTIVE

This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application's impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text.

METHODS

A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers.

RESULTS

When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing.

CONCLUSIONS

AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.

ChatGPT and the Future of Journal Reviews: A Feasibility Study

The increasing volume of research submissions to academic journals poses a significant challenge for traditional peer-review processes. To address this issue, this study explores the potential of employing ChatGPT, an advanced large language model (LLM), developed by OpenAI, as an artificial intelligence (AI) reviewer for academic journals. By leveraging the vast knowledge and natural language processing capabilities of ChatGPT, we hypothesize it may be possible to enhance the efficiency, consistency, and quality of the peer-review process. This research investigated key aspects of integrating ChatGPT into the journal review workflow. We compared the critical analysis of ChatGPT, acting as an AI reviewer, to human reviews for a single published article. Our methodological framework involved subjecting ChatGPT to an intricate examination, wherein its evaluative acumen was juxtaposed against human-authored reviews of a singular published article. As this is a feasibility study, one article was reviewed, which was a case report on scurvy. The entire article was used as an input into ChatGPT and commanded it to "Please perform a review of the following article and give points for revision." Since this was a case report with a limited word count the entire article could fit in one chat box. The output by ChatGPT was then compared with the comments by human reviewers. Key performance metrics, including precision and overall agreement, were judiciously and subjectively measured to portray the efficacy of ChatGPT as an AI reviewer in comparison to its human counterparts. The outcomes of this rigorous analysis unveiled compelling evidence regarding ChatGPT's performance as an AI reviewer. We demonstrated that ChatGPT's critical analyses aligned with those of human reviewers, as evidenced by the inter-rater agreement. Notably, ChatGPT exhibited commendable capability in identifying methodological flaws, articulating insightful feedback on theoretical frameworks, and gauging the overall contribution of the articles to their respective fields. While the integration of ChatGPT showcased immense promise, certain challenges and caveats surfaced. For example, ambiguities might present with complex research articles, leading to nuanced discrepancies between AI and human reviews. Also figures and images cannot be reviewed by ChatGPT. Lengthy articles need to be reviewed in parts by ChatGPT as the entire article will not fit in one chat/response. The benefits consist of reduction in time needed by journals to review the articles submitted to them, as well as an AI assistant to give a different perspective about the research papers other than the human reviewers. In conclusion, this research contributes a groundbreaking foundation for incorporating ChatGPT into the pantheon of journal reviewers. The delineated guidelines distill key insights into operationalizing ChatGPT as a proficient reviewer within academic journal frameworks, paving the way for a more efficient and insightful review process.

Best Practices for Using AI Tools as an Author, Peer Reviewer, or Editor

The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community. Generative AI has computationally become capable of elaborating research questions; refining programming code; generating text in scientific language; and generating images, graphics, or figures. However, this technology should be used with caution. In this editorial, we outline the current state of editorial policies on generative AI or chatbot use in authorship, peer review, and editorial processing of scientific and scholarly manuscripts. Additionally, we provide JMIR Publications' editorial policies on these issues. We further detail JMIR Publications' approach to the applications of AI in the editorial process for manuscripts in review in a JMIR Publications journal.