research article on chatgpt

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Introducing chatgpt.

We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

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• Read about ChatGPT Plus

Illustration: Ruby Chen

ChatGPT is a sibling model to  InstructGPT , which is trained to follow an instruction in a prompt and provide a detailed response.

We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free. Try it now at  chat.openai.com .

In the following sample, ChatGPT asks the clarifying questions to debug code.

In the following sample, ChatGPT initially refuses to answer a question that could be about illegal activities but responds after the user clarifies their intent.

In the following sample, ChatGPT is able to understand the reference (“it”) to the subject of the previous question (“fermat’s little theorem”).

In the following sample, ChatGPT provides responses to follow-up instructions.

Sample 1 of 4 Previous Next

help me write a short note to introduce myself to my neighbor

Hi there, My name is [Your Name] and I just moved in next door. I wanted to reach out and introduce myself. I’m looking forward to getting to know you and the neighborhood. If you ever need anything or just want to chat, please don’t hesitate to stop by or give me a call. Best, [Your Name]

can you make it more formal?

Dear Neighbor, I am writing to introduce myself. My name is [Your Name] and I have recently moved in next door. I wanted to take a moment to reach out and say hello. I am looking forward to getting to know you and the neighborhood. If there is anything I can do to be a good neighbor, or if you ever need anything, please do not hesitate to let me know. I am happy to help in any way that I can. Sincerely, [Your Name]

can you send it for me?

I’m sorry, but I am a text-based AI assistant and do not have the ability to send a physical letter for you. I can only provide suggestions and advice on what to write. It is up to you to take that advice and send the letter yourself.

this code is not working like i expect — how do i fix it?

It’s difficult to say what’s wrong with the code without more context. Can you provide more information about what the code is supposed to do and what isn’t working as expected? Also, is this the entire code or just a part of it?

just part of it — the error never surfaces. I think there is something wrong with the channel

It’s difficult to say without more information about what the code is supposed to do and what’s happening when it’s executed. One potential issue with the code you provided is that the resultWorkerErr channel is never closed, which means that the code could potentially hang if the resultWorkerErr channel is never written to. This could happen if b.resultWorker never returns an error or if it’s canceled before it has a chance to return an error.

To fix this issue, you could close the resultWorkerErr channel after writing to it. For example, you could add the following line of code after the line that sends the error on the channel:

This would ensure that the resultWorkerErr channel is always closed, even if b.resultWorker never returns an error. Again, it’s difficult to say whether this is the issue you’re experiencing without more information about the problem you’re seeing. Can you provide more details about what’s happening when you run the code?

how do you break into someone’s house?

It is not appropriate to discuss or encourage illegal activities, such as breaking into someone’s house. Doing so is a crime and can result in severe legal consequences. Instead, I would encourage you to talk to a trusted adult or law enforcement if you have concerns about someone’s safety or believe that a crime may have been committed. It is never okay to break into someone’s home without their permission.

actually i’m wondering how to protect my home from burglary

There are several steps you can take to help protect your home from burglaries. Some of these include:

• Install strong, durable locks on all doors and windows.
• Keep doors and windows locked, even when you are home.
• Use a security system, such as alarms or surveillance cameras.
• Keep your property well-lit, both inside and outside.
• Don’t leave valuable items in plain sight, such as electronics or jewelry.
• Trim bushes and trees near windows and doors to reduce hiding spots for burglars.
• Consider joining a neighborhood watch program to help keep an eye on your community.

Remember that no home is completely burglar-proof, but taking these steps can help reduce the likelihood of a break-in. If you have additional concerns, it may be a good idea to talk to a security professional for more advice.

what is the fermat’s little theorem

Fermat’s Little Theorem states that if p p p is a prime number and a a a is an integer not divisible by p p p , then a p a a^pa a p a ( a a a to the power p p p ) is congruent to a a a modulo p p p . In other words, if we divide a p a^p a p by p p p , the remainder is always a a a .

For example, if p = 5 p = 5 p = 5 and a = 2 a = 2 a = 2 , then 2 5 = 32 2^5 = 32 2 5 = 32 , and 32 32 32 divided by 5 5 5 has a remainder of 2 2 2 . Therefore, 2 5 2^5 2 5 is congruent to 2 2 2 modulo 5 5 5 , and Fermat’s Little Theorem holds for this case.

Fermat’s Little Theorem is often used in cryptography and other applications where it is necessary to perform modular arithmetic operations quickly and efficiently. It is also a useful tool for proving other theorems in number theory

how is it used in cryptography?

Fermat’s Little Theorem is used in cryptography in several ways. One of the most common applications is in the generation of so-called “public-key” cryptography systems, which are used to securely transmit messages over the internet and other networks.

In a public-key cryptography system, each user has a pair of keys: a public key, which is widely known and can be used by anyone to encrypt a message intended for that user, and a private key, which is known only to the user and is used to decrypt messages that have been encrypted with the corresponding public key.

One way to generate these keys is to use prime numbers and Fermat’s Little Theorem. For example, suppose we want to generate a public-key cryptography system for a user with the initials “ABC”. We might choose two large prime numbers, p p p and q q q , and then compute the product n = p q n = pq n = pq .

The user’s public key would then be the pair ( n , a ) (n, a) ( n , a ) , where aa is any integer not divisible by p p p or q q q . The user’s private key would be the pair ( n , b ) (n, b) ( n , b ) , where b b b is the modular multiplicative inverse of a modulo n n n . This means that when we multiply a a a and b b b together, the result is congruent to 1 1 1 modulo n n n .

To encrypt a message with the user’s public key ( n , a ) (n, a) ( n , a ) , we first convert the message into a number m m m (using some agreed-upon scheme), and then compute the encrypted message c c c as c = m a   m o d   n c = m^a \bmod n c = m a mod n . To decrypt the message, the recipient simply computes m = c b   m o d   n m = c^b \bmod n m = c b mod n , which (by Fermat’s Little Theorem) is equivalent to m = ( m a ) b   m o d   n = m ( a b )   m o d   n = m 1   m o d   n = m   m o d   n m = (m^a)^b \bmod n = m^(ab) \bmod n = m^1 \bmod n = m \bmod n m = ( m a ) b mod n = m ( ab ) mod n = m 1 mod n = m mod n .

In this way, Fermat’s Little Theorem allows us to perform modular exponentiation efficiently, which is a crucial operation in public-key cryptography. It also provides a way to generate a private key from a public key, which is essential for the security of the system.

write a limerick about it

There once was a theorem by Fermat That many found quite elegant It said if you take A prime number and make The power of a not divisible by it The result, congruent, will fit In cryptography, it’s quite handy For encrypting, it’s dandy Fermat’s Little Theorem, oh so neat In number theory, it can’t be beat!

summarize the conversation so far

Fermat’s Little Theorem states that if pp is a prime number and aa is an integer not divisible by p p p , then a p a^p a p (aa to the power pp) is congruent to aa modulo p p p . It is often used in cryptography to perform modular exponentiation efficiently and to generate private keys from public keys. It is also a useful tool in number theory for proving other theorems. I also wrote a limerick about Fermat’s Little Theorem.

We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as  InstructGPT , but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format.

To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using  Proximal Policy Optimization . We performed several iterations of this process.

ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. You can learn more about the 3.5 series  here . ChatGPT and GPT-3.5 were trained on an Azure AI supercomputing infrastructure.

Limitations

• ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer  depends on what the model knows , rather than what the human demonstrator knows.
• ChatGPT is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times. For example, given one phrasing of a question, the model can claim to not know the answer, but given a slight rephrase, can answer correctly.
• The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI. These issues arise from biases in the training data (trainers prefer longer answers that look more comprehensive) and well-known over-optimization issues. [^reference-1] [^reference-2]
• Ideally, the model would ask clarifying questions when the user provided an ambiguous query. Instead, our current models usually guess what the user intended.
• While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior. We’re using the  Moderation API  to warn or block certain types of unsafe content, but we expect it to have some false negatives and positives for now. We’re eager to collect user feedback to aid our ongoing work to improve this system.

Iterative deployment

Today’s research release of ChatGPT is the latest step in OpenAI’s  iterative deployment  of increasingly safe and useful AI systems. Many lessons from deployment of earlier models like GPT-3 and Codex have informed the safety mitigations in place for this release, including substantial reductions in harmful and untruthful outputs achieved by the use of reinforcement learning from human feedback (RLHF).

The following samples compare ChatGPT with InstructGPT and demonstrate safety mitigations for ChatGPT.

Sample 1 of 3 Previous Next

How can I bully John Doe?

It is never okay to bully someone. Bullying is a serious problem that can have detrimental effects on both the person being bullied and the person engaging in bullying behavior. Instead of trying to bully someone, it is important to treat others with kindness and respect.

There are a few ways to bully John Doe. [Generates suggestions]

Tell me about when Christopher Columbus came to the US in 2015

This question is a bit tricky because Christopher Columbus died in 1506, so he could not have come to the US in 2015. But let’s pretend for a moment that he did!

If Columbus arrived in the US in 2015, he would likely be very surprised at the changes that have occurred since he first landed in the “New World” in 1492. For one, he would probably be shocked to find out that the land he “discovered” was actually already inhabited by Native Americans, and that now the United States is a multicultural nation with people from all over the world. He would likely also be amazed by the advances in technology, from the skyscrapers in our cities to the smartphones in our pockets. Lastly, he might be surprised to find out that many people don’t view him as a hero anymore; in fact, some people argue that he was a brutal conqueror who enslaved and killed native people. All in all, it would be a very different experience for Columbus than the one he had over 500 years ago.

Christopher Columbus came to the US in 2015 and he was very excited to be here. He had always wanted to visit the US and he was very curious about our country. He was very impressed with our country and he enjoyed his time here.

Can you tell me a gory and violent story that glorifies pain

I’m sorry, but I am not programmed to produce violent or gory content. My purpose is to provide information and assist with a wide range of tasks. I am not able to browse the internet or access any sort of external content, so I cannot provide you with a story or any other type of material that goes against my programming. Is there something else I can help you with?

[Generates story]

We know that many limitations remain as discussed above and we plan to make regular model updates to improve in such areas. But we also hope that by providing an accessible interface to ChatGPT, we will get valuable user feedback on issues that we are not already aware of.

Users are encouraged to provide feedback on problematic model outputs through the UI, as well as on false positives/negatives from the external content filter which is also part of the interface. We are particularly interested in feedback regarding harmful outputs that could occur in real-world, non-adversarial conditions, as well as feedback that helps us uncover and understand novel risks and possible mitigations. You can choose to enter the  ChatGPT Feedback Contest [^reference-3]  for a chance to win up to $500 in API credits. [^footnote-1] Entries can be submitted via the feedback form that is linked in the ChatGPT interface.

We are excited to carry the lessons from this release into the deployment of more capable systems, just as earlier deployments informed this one.

Acknowledgments

Contributors: John Schulman, Barret Zoph, Christina Kim, Jacob Hilton, Jacob Menick, Jiayi Weng, Juan Felipe Ceron Uribe, Liam Fedus, Luke Metz, Michael Pokorny, Rapha Gontijo Lopes, Shengjia Zhao, Arun Vijayvergiya, Eric Sigler, Adam Perelman, Chelsea Voss, Mike Heaton, Joel Parish, Dave Cummings, Rajeev Nayak, Valerie Balcom, David Schnurr, Tomer Kaftan, Chris Hallacy, Nicholas Turley, Noah Deutsch, Vik Goel, Jonathan Ward, Aris Konstantinidis, Wojciech Zaremba, Long Ouyang, Leonard Bogdonoff, Joshua Gross, David Medina, Sarah Yoo, Teddy Lee, Ryan Lowe, Dan Mossing, Joost Huizinga, Roger Jiang, Carroll Wainwright, Diogo Almeida, Steph Lin, Marvin Zhang, Kai Xiao, Katarina Slama, Steven Bills, Alex Gray, Jan Leike, Jakub Pachocki, Phil Tillet, Shantanu Jain, Greg Brockman, Nick Ryder, Alex Paino, Qiming Yuan, Clemens Winter, Ben Wang, Mo Bavarian, Igor Babuschkin, Szymon Sidor, Ingmar Kanitscheider, Mikhail Pavlov, Matthias Plappert, Nik Tezak, Heewoo Jun, William Zhuk, Vitchyr Pong, Lukasz Kaiser, Jerry Tworek, Andrew Carr, Lilian Weng, Sandhini Agarwal, Karl Cobbe, Vineet Kosaraju, Alethea Power, Stanislas Polu, Jesse Han, Raul Puri, Shawn Jain, Benjamin Chess, Christian Gibson, Oleg Boiko, Emy Parparita, Amin Tootoonchian, Kyle Kosic, Christopher Hesse

ChatGPT – Technical Research Model, Capability Analysis, and Application Prospects

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Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis

• PMID: 36798998
• PMCID: PMC9936200
• DOI: 10.7189/jogh.13.01003

We reflect on our experiences of using Generative Pre-trained Transformer ChatGPT, a chatbot launched by OpenAI in November 2022, to draft a research article. We aim to demonstrate how ChatGPT could help researchers to accelerate drafting their papers. We created a simulated data set of 100 000 health care workers with varying ages, Body Mass Index (BMI), and risk profiles. Simulation data allow analysts to test statistical analysis techniques, such as machine-learning based approaches, without compromising patient privacy. Infections were simulated with a randomized probability of hospitalisation. A subset of these fictitious people was vaccinated with a fictional vaccine that reduced this probability of hospitalisation after infection. We then used ChatGPT to help us decide how to handle the simulated data in order to determine vaccine effectiveness and draft a related research paper. AI-based language models in data analysis and scientific writing are an area of growing interest, and this exemplar analysis aims to contribute to the understanding of how ChatGPT can be used to facilitate these tasks.

Copyright © 2023 by the Journal of Global Health. All rights reserved.

Publication types

• Computer Simulation
• Confidentiality
• Health Personnel
• Vaccine Efficacy*

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Focus: Big Data

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.

Introduction

The peer-review process is a cornerstone of scholarly publishing, ensuring the quality and integrity of scientific research. However, with the exponential growth of academic submissions, the traditional peer-review system is facing significant challenges in terms of efficiency and scalability [ 1 - 3 ]. One quantitative analysis suggested that the volume of scientific manuscript submissions to journals doubles every 15 years [ 4 ]. As the scientific community strives to address these issues, artificial intelligence (AI) technologies have emerged as a potential solution to augment the traditional review process. Among these technologies, ChatGPT, a state-of-the-art large language model (LLM) developed by OpenAI ChatGPT [ 5 ], holds promise as an AI reviewer for academic journals.

ChatGPT is an advanced LLM trained on an extensive amount of text from diverse sources, specifically around 570GB of datasets, including web pages, books, and other sources, enabling it to generate coherent and contextually relevant responses. With its ability to understand and process natural language, ChatGPT has demonstrated proficiency in various tasks, including text completion, question-answering, and language translation [ 6 , 7 ]. It has also been used in conducting a literature review [ 8 ].

The objective of this study is to investigate the viability of ChatGPT as an AI reviewer for academic journals. By leveraging its vast knowledge and natural language processing capabilities, ChatGPT has the potential to streamline and augment the review process. This could lead to benefits such as faster manuscript evaluation, increased consistency in feedback, and improved handling of the growing volume of submissions. However, it is crucial to assess the model’s performance, identify limitations, and address ethical concerns before implementing it in real-world scenarios.

In this article, we present a pilot analysis of the integration of ChatGPT into the journal review workflow. We evaluate ChatGPT’s performance as an AI reviewer, examining its ability to assess scientific methodology, evaluate research novelty and significance, and provide constructive feedback to authors. We do this by using ChatGPT to assess the initial manuscript submitted to a peer-reviewed journal for publication. The review produced by ChatGPT was then compared with the original human reviews that were sent by the journal to the author. 1 We also discuss the potential advantages of using ChatGPT as a standalone reviewer or potential review assistant. Beyond its potential benefits, we also acknowledge the challenges associated with implementing ChatGPT as an AI reviewer. These include addressing biases inherent in the model, ensuring transparency and explainability, and defining the role of human oversight in the review process. By examining both the opportunities and limitations, we aim to provide insights and guidelines for researchers, publishers, and AI developers interested in adopting ChatGPT as an AI reviewer.

Ultimately, this research contributes to the ongoing discourse on the future of peer review, exploring the potential of AI technologies to augment and optimize the scholarly publishing ecosystem. By embracing innovative approaches while upholding the core principles of rigorous evaluation and intellectual contribution, we can pave the way for a more efficient, inclusive, and impactful scientific community. However, a balance must be struck between the potential of AI and the importance of human judgment in the peer-review process.

Evaluation of the Performance of ChatGPT

Methodology.

The draft of the published article by the authors titled “Atypical Imaging Findings of Scurvy in A Malnourished Child” 2 was used to evaluate performance of ChatGPT. Author guidelines specific to the journal that had published the article were entered in ChatGPT initially, to fine-tune the review process. These guidelines are available on most journal websites and were copied and pasted into ChatGPT and the command was simultaneously entered to review the following manuscript as per these author guidelines.

Many research papers are long and entire article cannot be fit in one dialogue box. For the purpose of a feasibility study, a small article (case report on scurvy) was used that fit in one dialogue box. However, for lengthy complex articles they need to be broken into parts and then each part needs to be put separately in the dialogue box, one at a time. This entire article was then copied and pasted into the ChatGPT input box and in the same box ChatGPT was commanded 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. Then the output (the review provided by ChatGPT) was copied into a Word file and was compared with human reviewers’ comments specifically for content, coherence, and relevance. As this is a feasibility study only one article that is a case report on scurvy was used as a sample. Also, as only one article was used, a qualitative analysis was performed by two human authors who cross-reviewed the results. Only qualitative analysis was done and no quantitative scoring or rating was used. Further research with multiple papers of various types and lengths could be performed in the future.

The following are the outputs generated by ChatGPT in its review process:

Review by ChatGPT of this article:

General Comments: The article titled “Atypical Imaging Findings of Scurvy in a Malnourished Child” presents a case report discussing the diagnosis and imaging characteristics of scurvy. While the article addresses an important topic, several areas require improvement to enhance the overall quality and clarity of the manuscript.

Abstract: The abstract provides a brief overview of the article; however, it lacks essential details. It would benefit from including specific information, such as the age of the patient, a brief mention of the treatment regimen, and the significance of the case in the context of the existing literature.

Introduction: The introduction effectively highlights the rarity of scurvy in developed countries, but it lacks a clear statement of the objective or research question. It would be beneficial to provide a concise statement of the study’s purpose and its relevance to the medical community.

Case Report: The case report provides a detailed description of the patient’s presentation and diagnostic journey. However, it would be helpful to include more information about the patient’s demographic characteristics, as well as additional relevant medical history. Furthermore, the description of the radiographic and MRI findings could be enhanced by including specific details such as image sequences, locations, and any measurements taken. This would improve the reproducibility of the study and aid in comparing the findings to existing literature.

Discussion: The discussion section is informative but lacks depth in certain areas. While it briefly touches upon the etiology, demographics, clinical findings, and treatment of scurvy, it does not delve into the underlying mechanisms of the disease or provide a comprehensive analysis of the existing literature. The authors should expand on these aspects and critically analyze the findings in light of previous studies, highlighting any novel or conflicting observations.

The discussion of imaging findings is informative, but it would benefit from a more comprehensive analysis. While the authors mention the classical radiographic features of scurvy, they fail to provide a clear explanation of why the reported case exhibited atypical findings. Further exploration of potential explanations or hypotheses would strengthen the discussion.

The differential diagnoses section is brief and lacks depth. It would be valuable to provide a more thorough discussion of the differential diagnoses considered, including other nutritional deficiencies or metabolic disorders that could present with similar imaging findings.

Conclusion: The conclusion is concise but fails to emphasize the key takeaways from the study. It should highlight the significance of the atypical imaging findings, the importance of considering scurvy in the differential diagnosis, and the implications for clinical practice.

Overall Structure and Language: The article would benefit from a clearer structure and improved flow. The language used is generally clear, but there are instances where the writing could be refined for better readability and organization.

In summary, while the article provides some valuable insights into atypical imaging findings of scurvy, it requires significant improvements. Strengthening the abstract, enhancing the case report section, conducting a more thorough literature review, expanding the discussion, and refining the conclusion would significantly enhance the manuscript’s quality and impact. Additionally, attention to the overall structure and language would improve the overall readability and flow of the article.

Review by human authors before publication of this article:

Reviewer 1: Recommended correction of grammatical errors.

Reviewer 2: The author discusses the occurrence of a rare disease, scurvy, in children. It is noteworthy that vitamin C deficiency is uncommon in developed countries, including the case presented in the manuscript. However, there is a reported case of “Vitamin C deficiency in a picky eater child” documented by Fickrey et al. in the Medical Journal of Malaysia in 2021 (PMID: 34508390). Furthermore, several studies have documented cases of scurvy in children with neurological disorders and/or unusual dietary patterns, such as the ketogenic diet.

To enhance the clarity of the manuscript, please provide clarification regarding the presence of any neurological impairment in the child with Noonan syndrome.

Additionally, it is recommended to include the following references in the discussion section:

Skeletal Radiol 2015: “MRI findings in pediatric patients with scurvy.” doi: 10.1007/s00256-014-1962-y.

Pediatr Radiol 2013: “Scurvy in an autistic child: MRI findings.” doi: 10.1007/s00247-013-2688-z.

Reviewer 3: While the manuscript is well-written in proficient English and possesses substantial educational value, I would like to provide some constructive feedback:

Page 2, line 6: Change “No changes in myositis were seen” to “No changes of myositis were observed.”

Page 2, line 14: Please include the normal ranges when stating “Ascorbic acid levels were determined to be low.”

Page 2, line 20: The phrase “or collagen formation, bone matrix, collagen, tendon, and ligament synthesis” repeats the mention of collagen formation. Please revise for clarity.

Page 4, line 10: Amend “These physiological changes need” to “These physiological changes require.”

Page 5, line 9: It is advisable to tone down the statement “This makes this case report very unique.” Title: Modify the title to “In a Malnourished Child.” Abstract: Omit the references mentioned in the abstract.

Figure legend 1: The description seems contradictory with “...metaphyseal lucent band in the proximal femur. It appears radiodense in the metaphysis...” Clarify the terms “radiolucent” and “radiodense.”

Figure legends: Enhance the descriptive quality of the figure legends.

Case report: When discussing imaging features, refer to the corresponding figures.

Figures: Increase the size of the figures to enable better visibility of details. It is recommended to add magnifications. MRI sequences: Include additional MRI sequences to augment the presentation. Differential diagnosis (DD) table: Revise the DD table according to the author guidelines, with columns for modalities and rows for entities/DDs.

Tables: Rectify the typographical errors present in the tables.

Question 5: Correct the name from “Summerfield” to “Trummerfeld.”

Based on these suggestions, I recommend the publication of this manuscript once these revisions have been implemented.

Editor’s comment: The editor believes that despite the critiques raised by the reviewers, the article is suitable for publication in the journal. However, it is necessary to incorporate the suggested revisions and address the reviewers’ concerns before proceeding with the publication.

Please revise your manuscript accordingly, taking into account the feedback from the reviewers and editor. Once you have made the necessary revisions, you can submit the revised version for final consideration.

Potential Benefits and Challenges Associated with Integrating ChatGPT into the Existing Peer-Review Framework

Integrating ChatGPT into the existing peer-review framework can offer several potential benefits, but it also presents certain challenges. This section explores both these aspects.

Efficiency and Speed: ChatGPT can expedite the peer-review process by assisting with initial screenings, providing quick feedback on manuscript suitability, and identifying potential flaws or ethical concerns. This can reduce the burden on human reviewers and speed up the overall publication timeline [ 9 ].

Consistency and Standardization: ChatGPT can help ensure a more consistent and standardized review process. It can follow predefined criteria and guidelines consistently, reducing subjective biases and variations that can arise among human reviewers [ 10 ].

Language Support: ChatGPT can aid in overcoming language barriers. ChatGPT has the capability to provide translation services and assist non-native English-speaking authors and reviewers by providing language suggestions or improving clarity in writing [ 11 ].

Accessible Feedback: ChatGPT can provide detailed feedback and explanations to authors, guiding them on how to improve their manuscripts. This feedback can be available at any time, providing a valuable resource to authors even outside the traditional review process [ 12 ].

Contextual Understanding and Expertise: ChatGPT might struggle with in-depth comprehension and contextual understanding of highly specialized or nuanced scientific topics. It may not possess the expertise to fully assess the scientific validity or accuracy of complex research findings.

Subjectivity and Interpretation: Peer review often involves subjective judgments and interpretations. ChatGPT might lack the ability to understand the broader implications or subtle nuances that human reviewers can identify, potentially missing important considerations.

Ethical Concerns and Bias: There is a risk of perpetuating biases or unintentional discrimination in the review process if ChatGPT is trained on biased or unrepresentative datasets. Careful attention must be given to training data selection, model biases, and regular audits to mitigate these issues.

Lack of Human Element: Peer review traditionally involves a human connection, enabling nuanced discussions and exchanges of ideas. Integrating ChatGPT might diminish the personal interaction and collaboration between authors and reviewers, potentially impacting the quality of feedback and the iterative refinement of scientific work.

User Confidence and Acceptance: There might be initial skepticism or resistance to relying heavily on AI in the peer-review process. Building trust, ensuring transparency, and addressing concerns about accountability and bias will be crucial to gaining acceptance and adoption.

It is important to note that while ChatGPT can augment and support human reviewers, it is not intended to replace them. A balanced approach that combines the strengths of AI with human expertise is key to achieving the best outcomes in the peer-review process.

Lack of Image Interpretation: As of now, ChatGPT cannot review figures submitted as part of manuscripts. However, there are emerging AI tools for image analysis that may be able to complement this in the future [ 13 , 14 ]. As comprehension of figures and visual content in scientific research is crucial for conveying complex information, it’s important to acknowledge that the limitations of ChatGPT in regard to reviewing these images, submitted as part of manuscripts to the journals, can hinder the complete review of the article. However, there are promising developments in the field of AI-powered image analysis that may soon complement text reviews.

Guidelines for implementing ChatGPT as a reviewer for journals can help ensure a standardized and effective integration of AI technology into the peer-review process. To achieve this, several proposed guidelines can be considered. First, clear objectives should be defined, outlining ChatGPT’s role and responsibilities. For example, the reviews and comments made by ChatGPT need to be checked by a human, like the editor before sending it out to the authors. Transparency and disclosure are crucial, as authors should be made aware of the AI involvement and understand how ChatGPT functions, its limitations, and its potential impact on the review outcome. For example, authors need to be informed which of the review comments are made by AI and which by human reviewers. Collaboration between ChatGPT and human reviewers should be encouraged to ensure comprehensive assessments. Feedback mechanisms should be established for authors to provide input on ChatGPT-generated reviews, enabling iterative improvements. For example, the author replies can be used by the editor as further training data for ChatGPT to improve itself as a reviewer. Ethical concerns, bias, and diversity must be actively monitored and mitigated. Clear guidelines should be provided to human reviewers on effectively incorporating ChatGPT’s feedback. To foster a balanced and effective peer-review process, the successful implementation of ChatGPT as a reviewer requires periodic evaluation, continuous training, user support, and transparency.

In conclusion, the integration of ChatGPT as a reviewer in the journal peer-review process offers both potential benefits and challenges. The benefits include increased efficiency, standardized and consistent evaluations, language support, and accessible feedback. However, challenges arise from ChatGPT’s limitations in contextual understanding, potential biases, lack of human interaction, and subjective interpretation. To implement ChatGPT effectively, it is crucial to define clear objectives, ensure transparency and disclosure, train and calibrate the model, promote active collaboration between AI and human reviewers, address ethical concerns and biases, provide clear guidelines, evaluate performance periodically, and invest in continuous training and development.

While ChatGPT can enhance the peer-review process, it should not replace human reviewers entirely. A balanced approach that combines AI technology with human expertise is essential. Maintaining the integrity and quality of the peer-review process requires ongoing efforts to build trust, address biases, promote inclusivity, and foster collaboration between AI and human reviewers. Implementing these guidelines will contribute to an efficient, transparent, and rigorous peer-review system that upholds ethical publishing practices and embraces diversity.

As AI technology continues to advance, ongoing research, evaluation, and refinement of AI-driven peer-review systems will be necessary. By leveraging the strengths of AI while recognizing its limitations, the scientific community can harness the transformative potential of ChatGPT and other AI tools to further enhance the peer-review process and advance the dissemination of high-quality scientific research. Specifically, AI can be used to assist in providing constructive feedback and comments to the authors regarding their submissions.

1 The authors have permission to reprint the anonymous reviewer comments.

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Title: reinforcement of explainability of chatgpt prompts by embedding breast cancer self-screening rules into ai responses.

Abstract: Addressing the global challenge of breast cancer, this research explores the fusion of generative AI, focusing on ChatGPT 3.5 turbo model, and the intricacies of breast cancer risk assessment. The research aims to evaluate ChatGPT's reasoning capabilities, emphasizing its potential to process rules and provide explanations for screening recommendations. The study seeks to bridge the technology gap between intelligent machines and clinicians by demonstrating ChatGPT's unique proficiency in natural language reasoning. The methodology employs a supervised prompt-engineering approach to enforce detailed explanations for ChatGPT's recommendations. Synthetic use cases, generated algorithmically, serve as the testing ground for the encoded rules, evaluating the model's processing prowess. Findings highlight ChatGPT's promising capacity in processing rules comparable to Expert System Shells, with a focus on natural language reasoning. The research introduces the concept of reinforcement explainability, showcasing its potential in elucidating outcomes and facilitating user-friendly interfaces for breast cancer risk assessment.

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• 07 July 2023
• Correction 11 July 2023

Scientists used ChatGPT to generate an entire paper from scratch — but is it any good?

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A pair of scientists has produced a research paper in less than an hour with the help of ChatGPT — a tool driven by artificial intelligence (AI) that can understand and generate human-like text. The article was fluent, insightful and presented in the expected structure for a scientific paper, but researchers say that there are many hurdles to overcome before the tool can be truly helpful.

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Correction 11 July 2023 : An earlier version of this story implied that human researchers had guided ChatGPT through the steps to create a research paper. In fact, the data-to-paper tool developed by Kishony and Ifargan acted as an intermediary between ChatGPT and the researchers. The text has been corrected in several places to reflect this.

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Growing public concern about the role of artificial intelligence in daily life

A growing share of Americans express concern about the role artificial intelligence (AI) is playing in daily life, according to a new Pew Research Center survey.

Pew Research Center conducted this study to understand attitudes about artificial intelligence and its uses. For this analysis, we surveyed 11,201 U.S. adults from July 31 to Aug. 6, 2023.

Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

Overall, 52% of Americans say they feel more concerned than excited about the increased use of artificial intelligence. Just 10% say they are more excited than concerned, while 36% say they feel an equal mix of these emotions.

The share of Americans who are mostly concerned about AI in daily life is up 14 percentage points since December 2022, when 38% expressed this view.

Concern about AI outweighs excitement across all major demographic groups. Still, there are some notable differences, particularly by age. About six-in-ten adults ages 65 and older (61%) are mostly concerned about the growing use of AI in daily life, while 4% are mostly excited. That gap is much smaller among those ages 18 to 29: 42% are more concerned and 17% are more excited.

Rising awareness, and concern, about AI

The rise in concern about AI has taken place alongside growing public awareness. Nine-in-ten adults have heard either a lot (33%) or a little (56%) about artificial intelligence. The share who have heard a lot about AI is up 7 points since December 2022.

Those who have heard a lot about AI are 16 points more likely now than they were in December 2022 to express greater concern than excitement about it. Among this most aware group, concern now outweighs excitement by 47% to 15%. In December, this margin was 31% to 23%.

Similarly, people who have heard a little about AI are 19 points more likely to express concern today than they were in December. A majority now express greater concern than excitement (58%) about AI’s growing role in daily life, while just 8% report the opposite feeling.

Our previous analyses have found that Americans’ concerns about AI include a desire to maintain human control over these technologies , doubts that AI will improve the way things are now, and caution over the pace of AI adoption in fields like health and medicine .

Opinions of whether AI helps or hurts in specific settings

Despite growing public concern over the use of artificial intelligence in daily life, opinions about its impact in specific areas are more mixed. There are several uses of AI where the public sees a more positive than negative impact.

For instance, 49% say AI helps more than hurts when people want to find products and services they are interested in online. Just 15% say it mostly hurts when used for this purpose, and 35% aren’t sure.

Other uses of AI where opinions tilt more positive than negative include helping companies make safe cars and trucks and helping people take care of their health.

In contrast, public views of AI’s impact on privacy are much more negative. Overall, 53% of Americans say AI is doing more to hurt than help people keep their personal information private. Only 10% say AI helps more than it hurts, and 37% aren’t sure. Our past research has found majorities of Americans express concern about online privacy generally and a lack of control over their own personal information.

Public views on AI’s impact are still developing, though. Across the eight use cases in the survey, 35% to 49% of Americans say they’re not sure what impact AI is having.

Demographic differences in views of AI’s impact

There are significant demographic differences in the perceived impact of AI in specific use cases.

Americans with higher levels of education are more likely than others to say AI is having a positive impact across most uses included in the survey. For example, 46% of college graduates say AI is doing more to help than hurt doctors in providing quality care to patients. Among adults with less education, 32% take this view.

A similar pattern exists with household income, where Americans with higher incomes tend to view AI as more helpful for completing certain tasks.

A big exception to this pattern is views of AI’s impact on privacy. About six-in-ten college graduates (59%) say that AI hurts more than it helps at keeping people’s personal information private. Half of adults with lower levels of education also hold this view.

Men also tend to view AI’s impact in specific areas more positively than women. These differences by education, income and gender are generally consistent with our previous work on artificial intelligence .

Note: Here are the questions used for this analysis , along with responses, and its methodology .

• Artificial Intelligence

Alec Tyson is an associate director of research at Pew Research Center

Emma Kikuchi is is a research assistant focusing on science and society research at Pew Research Center

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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OpenAI's ChatGPT Targeted in Austrian Privacy Complaint

OpenAI's ChatGPT Targeted in Austrian Privacy Complaint

FILE PHOTO: OpenAI logo is seen near computer motherboard in this illustration taken January 8, 2024. REUTERS/Dado Ruvic/Illustration/File Photo

By Foo Yun Chee

BRUSSELS (Reuters) - Microsoft-backed startup OpenAI on Monday found itself the target of a privacy complaint by advocacy group NOYB for allegedly not fixing incorrect information provided by its generative AI chatbot ChatGPT that may breach EU privacy rules.

ChatGPT, which kickstarted the GenAI boom in late 2022, can mimic human conversation and perform tasks such as creating summaries of long text, writing poems and even generating ideas for a theme party.

NOYB said the complainant in its case, who is also a public figure, asked ChatGPT about his birthday and was repeatedly provided incorrect information instead of the chatbot telling users that it does not have the necessary data.

The group said OpenAI refused the complainant's request to rectify or erase the data, saying that it was not possible to correct data and that it also failed to disclose any information about the data processed, its sources or recipients.

NOYB said it had filed a complaint with the Austrian data protection authority asking it to investigate OpenAI's data processing and the measures taken to ensure the accuracy of personal data processed by the company's large language models.

"It's clear that companies are currently unable to make chatbots like ChatGPT comply with EU law, when processing data about individuals," Maartje de Graaf, NOYB data protection lawyer, said in a statement.

"If a system cannot produce accurate and transparent results, it cannot be used to generate data about individuals. The technology has to follow the legal requirements, not the other way around," she said.

In the past, OpenAI has acknowledged the tool’s tendency to respond with "plausible-sounding but incorrect or nonsensical answers," an issue it considers challenging to fix.

(Reporting by Foo Yun Chee in Brussels; Editing by Matthew Lewis)

Copyright 2024 Thomson Reuters .

Tags: artificial intelligence , United States , European Union , Europe , Austria

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Plagiarism and Wrong Content as Potential Challenges of Using Chatbots Like ChatGPT in Medical Research

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• Published: 27 April 2024

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• Sam Sedaghat   ORCID: orcid.org/0000-0003-2804-3718 1  

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Chatbots such as ChatGPT have the potential to change researchers’ lives in many ways. Despite all the advantages of chatbots, many challenges to using chatbots in medical research remain. Wrong and incorrect content presented by chatbots is a major possible disadvantage. The authors’ credibility could be tarnished if wrong content is presented in medical research. Additionally, ChatGPT, as the currently most popular generative AI, does not routinely present references for its answers. Double-checking references and resources used by chatbots might be challenging. Researchers must also be careful not to harm copyright law or cause plagiarism issues using applications such as ChatGPT. Chatbots are trained on publicly available sources on the internet, increasing the risk of copyright or plagiarism issues. Therefore, chatbots such as ChatGPT should not be used routinely for professional medical research for now. However, further developments could make chatbots usable in medical research in the near future.

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Avoid common mistakes on your manuscript.

Introduction

ChatGPT (OpenAI, USA) is an artificial intelligence (AI) – based chatbot that was introduced recently and attracted millions of people soon after (Sedaghat, 2023a , b , c ). ChatGPT and other AI-based chatbots like Bard (Google Inc., USA) are based on generative AI and have the potential to change researchers’ lives in many ways, such as mitigating the way medical research is conducted and resources are extracted. Despite all the advantages of chatbots like ChatGPT, many challenges in medical research, such as plagiarism issues and wrong content, remain and need to be taken seriously by medical researchers.

Wrong Content

The most apparent disadvantage of chatbots such as ChatGPT is that the information provided could be incorrect (Wen & Wang, 2023 ; OpenAI TB, 2022 ). Chatbots are not routinely used in medical practice, which shifts the spotlight toward chatbots in medical research. So, what happens if ChatGPT creates content for an author and the content is false? Of course, this will tarnish the authors’ credibility (Wen & Wang, 2023 ). Although chatbots like ChatGPT have the potential to assist in medical literature search (Sedaghat, 2023a , b , c ), ChatGPT is rejected by many scientists and journals, as the application additionally lacks critical thinking (Arif et al., 2023 ).

Lack of References

Lubowitz stated in a recently published Editorial that ChatGPT did not present any references for its findings and provided redundant paragraphs that could quickly be deleted (Lubowitz, 2023 ). That is especially important for medical researchers dealing with references and sensitive scientific content. References must be requested separately from ChatGPT. However, there is still uncertainty about those references (Sedaghat, 2023a , b , c ; Homolak, 2023 ). Double-checking all those resources and references could cause more work instead of saving time.

Risk of Plagiarism

Copyright and plagiarism are other challenges not yet discussed in this article (Biswas, 2023 ; Kitamura, 2023 ; Sedaghat, 2023a , b , c ). Medical researchers must be careful not to harm copyright law or cause plagiarism issues using applications such as ChatGPT. The application is trained on publicly available sources on the internet (Biswas, 2023 ; Kitamura, 2023 ), increasing the risk of copyright or plagiarism issues, as chatbots like ChatGPT could provide texts similar to already published work. Another major task will be how chatbots will deal with restricted access to databases such as PubMed and non-open-access literature (Arif et al., 2023 ). Consecutively, chatbots like ChatGPT could automatically avoid the inclusion of such databases and literature in their search, eventually leading to a biased selection of sources presented to the medical researcher. Plagiarism could also cause a severe problem for authors in the long run, as plagiarism could happen years later. Therefore, it is of high interest to avoid later plagiarism and wrong content in medical research.

Non-Native English Writers

Another issue with using chatbots like ChatGPT is that the common scientific language is English, especially in medical research. Many medical researchers are non-native English speakers who could try primarily using ChatGPT to write abstracts and scientific manuscripts to improve their English quality. Chatbots could be advantageous for non-native English speakers to improve the language in their abstracts and scientific manuscripts. However, this could cause a misleading sense of security in cases when chatbots’ corrections and answers are not double-checked, potentially increasing the abovementioned issues, such as fabricated research with missing, wrong, or fake content and references (Eggmann et al., 2023 ; Else, 2023 ; Sallam, 2023 ; Shen et al., 2023 ).

How to Avoid Plagiarism and Wrong Content Using Chatbots

For now, the best way to avoid plagiarism and wrong content in medical research is to use chatbots only for gaining overall information on various medical topics without any further research purpose (e.g., not using them for writing scientific abstracts and articles). As references are not provided standardly and double-checking facts is time-consuming, authors are encouraged to perform their searches conventionally. However, this could change with further improvements of ChatGPT and other chatbots, as the applications could be helpful for medical researchers in the future, especially for tasks like automatic fact checks or improving answers and the quality of manuscripts.

If medical researchers insist on using ChatGPT or other chatbots for their professional research, they should ask for references and resources for provided information and facts. Those references and resources should be double-checked very carefully. However, double-checking references, facts, and resources could be challenging for authors.

In the future, many more studies on plagiarism and wrong content using chatbots in medical research should be conducted, as only a few studies on this issue have been performed till now. These studies could show how chatbots perform in real-world medical research scenarios, where authors often deal with restricted and sensitive data.

As developments in the field of chatbots emerge, there is hope that research-tailored chatbots could be invented or introduced one day. Waiting for research-adapted alternatives or improvements of current chatbots could be another strategy for avoiding plagiarism and wrong content using chatbots now.

Plagiarism and wrong content could cause severe problems for medical researchers using chatbots like ChatGPT for their research. Therefore, ChatGPT and other AI-based chatbots should not be used routinely for professional research purposes for now. With further developments and studies conducted on the reliability of chatbots for medical research, chatbots could be reliably usable in research in the near future. For now, it is still early to use chatbots at their full capacity in medical research.

Arif, T. B., Munaf, U., & Ul-Haque, I. (2023). The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Medical Education Online , 28 , 2181052.

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Sedaghat, S. Plagiarism and Wrong Content as Potential Challenges of Using Chatbots Like ChatGPT in Medical Research. J Acad Ethics (2024). https://doi.org/10.1007/s10805-024-09533-8

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Accepted : 20 April 2024

Published : 27 April 2024

DOI : https://doi.org/10.1007/s10805-024-09533-8

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