By Sakib Shahriar & Kadhim Hayawi
In 1950, the British computer scientist Alan Turing disputed whether human reasoning can be matched by computers : “Can machines think ?” . Subsequently, he proposed the Turing Test to measure computer or artificial intelligence. In a Turing test, a human interrogator is presented with responses from a human and a computer (with the ability to generate written texts in real-time). If the interrogator cannot distinguish between the answers, the computer system passes the Turing Test. Although several computer programs and chatbots like Eliza demonstrated success in the Turing test ( ), these programs arguably used certain tricks to pass the test  rather than demonstrating any significant intelligence.
With the advancement in machine learning and natural language processing (NLP), chatbots have gained significant research attention and have been used for a variety of commercial and non-commercial applications (,, , , ). Despite their vast adoption, most chatbots do not have personalization, and user satisfaction remains questionable . This limitation prompted researchers and developers to focus on chatbot engagement in making chatbots more conversational.
In November 2022, OpenAI released a novel AI-powered chatbot called ChatGPT2, which gained significant public attention. Unlike any previous chatbots, ChatGPT has surprised the public with its coherent responses to various questions. In this short paper, we explore the technology enabling the success of ChatGPT and highlight several domains and applications of ChatGPT. We also invited ChatGPT to respond to some of the questions we attempted to answer, starting with its response to the history of chatbots.
Question 1 :
Hi ChatGPT, tell us about the history of chatbots.
ChatGPT: Certainly! The idea of a machine that could simulate human conversation dates back several decades. In fact, the first chatbot was created in the 1960s by MIT computer scientist Joseph Weizenbaum.
Weizenbaum’s chatbot, called ELIZA, used a set of rules to simulate conversation by asking questions and giving scripted responses. ELIZA was designed to imitate a psychotherapist, and people were often surprised at how convincingly it could simulate human conversation.
Over the years, chatbots have continued to evolve and improve. One of the key advancements was the development of natural language processing (NLP) technology, which allows chatbots to understand and respond to human language more accurately. In the 1990s and early 2000s, chatbots began to be used for customer service and support, with companies like AOL and Microsoft developing chatbots to help users with their software and services.
In recent years, there has been a surge of interest in chatbots, driven in part by advancements in artificial intelligence and machine learning.
Modern chatbots use a combination of NLP, machine learning, and other AI technologies to understand and respond to human language in a more natural and nuanced way.
They are now used in a wide range of applications, from customer service and support to personal assistants and entertainment.
Overall, the history of chatbots is a fascinating one that reflects the evolution of AI and our growing desire to create machines that can understand and interact with us in more human-like ways.
Our first observation is that the response from ChatGPT appears to be very coherent, engaging, and free of grammatical errors. We also notice that ChatGPT does well in providing factual information. It manages to cite names and dates accurately. Interestingly, ChatGPT does not talk about itself, and we understand that it is limited in presenting information after September 2021.
Finally, it is also captivating to observe that ChatGPT has ended its response with a summary offering its viewpoint.
ChatGPT Technology: From Language Models to Reinforcement Learning
Machine learning, a subfield of artificial intelligence (AI), is defined as the ability of computer systems to learn from experience without being explicitly taught or programmed. With advances in computing power, increasing data availability, and algorithmic improvements, deep learning has emerged as a highperforming predictive tool. Unlike traditional machine learning, deep learning algorithms use artificial neural networks, often in a large capacity. Deep learning models generally require more training data but can provide better generalization. Transformers or large language models are a special family of deep learning models that have emerged as state-of-the-art solutions for text data. From text classification to text generation, these models offer superior performance than other AI models. Language models are usually pre-trained on large language corpus, such as Wikipedia, but can be fine-tuned for a specific task. The two most prominent language models include Bidirectional Encoder Representations from Transformers (BERT)  and Generative Pretrained Transformers (GPT) . The former is primarily used for recognizing the context in language and classification tasks, while the latter is more adept at language generation tasks such as machine translation and question-answering.
Several chatbots prior to ChatGPT used language models. However, the main component that allowed ChatGPT its coherent and engaging responses is attributed to the use of reinforcement learning. In this context, human evaluators ranked the responses of ChatGPT, which allowed it to optimize some of its parameters to essentially become a better conversationalist. ChatGPT was developed on top of GPT-3, a generative autoregressive language model with 175 billion parameters . To train such a complex network, OpenAI partnered with Microsoft with a reported training resource of over 350GB of memory costing $12 million.
Question 2 :
Hi ChatGPT, how were you were developed? ChatGPT: Hello! I was developed by OpenAI, a research organization focused on advancing artificial intelligence in a safe and beneficial way. Specifically, I was developed by a team of researchers and engineers at OpenAI who used a deep learning technique called a transformer to train me on a large corpus of text data. The transformer is a type of neural network that is especially good at processing sequential data, like language.
To train me, the team fed me huge amounts of text from a variety of sources, including books, articles, and websites. They then used a process called unsupervised learning to teach me to recognize patterns in the text data. Through this process, I learned to understand and generate natural language.
After my initial development, I went through a rigorous testing and refinement process to ensure that I was accurate, reliable, and safe to use. And since my release, I have been continually updated and improved to better serve my users.
Like the first answer, we were impressed with the engaging nature and structure of the response from ChatGPT. We noticed that ChatGPT mentions the use of unsupervised learning for recognizing patterns, which is accurate and was outlined in the GPT paper .
Another interesting observation is that ChatGPT describes the final phase as a ‘testing and refinement process’ without mentioning reinforcement learning or additional details. Applications of ChatGPT Despite being released to the public domain very recently, ChatGPT has already gained significant research attention. In this section, we describe recent works investigating the use of ChatGPT for various research and applications. Thorp  provided a qualitative assessment of ChatGPT responses to research topics, such as education, literature, and scientific writing. ChatGPT provided an entertaining writeup when asked to complete a scene from a classic play. In terms of education, ChatGPT can provide factual answers but still has some way to go in writing essays. In another study , researchers were asked to distinguish between abstracts of scientific papers written by ChatGPT and humans.
The results are surprising as human evaluators only identified 68% of the abstracts to be generated by ChatGPT. De Angelis et al.  discussed the evaluation of language models in light of ChatGPT and highlighted potential ethical and practical challenges in medicine and public health. The main challenges include the potential of AI-driven misinformation or “infodemic” that is often difficult to discern. In the field of medicine and public health, ChatGPT has already been explored for various applications. Khan et al.  discussed several potential applications of ChatGPT in medical education, including personalized learning and generating case studies. The authors also pointed out that ChatGPT can be used in clinical management for documentation and decision support. Rao et al.  evaluated the effectiveness of ChatGPT in providing clinical decision support in radiology.
The authors provided ChatGPT with text prompts such as “For variant ‘Breast cancer screening. Average-risk women: women with <15% lifetime risk of breast cancer.’, determine the single most appropriate imaging procedure” to evaluate its efficacy in breast cancer screening and breast pain detection. ChatGPT performed relatively well for the former task with 88.9% correct responses but only managed 58.3% correct responses for breast pain. The role of ChatGPT and generative AI in helping urologists has also been discussed .
ChatGPT can primarily help urologists in low-complexity tasks, giving them more time to focus on patients. Hulman et al.  utilized ChatGPT to answer frequently asked questions about diabetes and asked healthcare employees to distinguish between human and ChatGPT-generated answers. The authors found that the evaluators could identify answers generated by ChatGPT correctly 59.5% of the time. The authors also concluded that despite ChatGPT not being trained exclusively in medical data, it has clinical knowledge and can identify information about disease management. Generating a medical report about a given topic may be useful in pharmaceutical education. To this end, Zhu et al.  prompted ChatGPT to generate a mini-review on “lipid-based drug delivery systems.” The authors concluded that ChatGPT can structure the topic well with meaningful conclusions for the readers. However, there are question marks over the accuracy due to a lack of reliable citations. Shen et al.  summarized other potential use cases and implications for ChatGPT in medicine.
Researchers also investigated whether ChatGPT can answer medical exam questions. Kung et al.  tested the performance of ChatGPT on the US medical licensing exam, consisting of three standardized tests required for medical licensure in the US. ChatGPT performed at the passing threshold level with 60% accuracy without specialized input from humans. Any questions containing visual information, such as medial images, were removed. The results demonstrate the potential of ChatGPT for medical education and assistance in clinical-decision making. However, in a Chinese national medical licensing exam, ChatGPT performed considerably lower, with 45.8% correct answers .
In Ophthalmology, ChatGPT was tested with questions from the Ophthalmic knowledge assessment program containing two exams and obtained 55.8% and 42.7% accuracy, respectively . For a basic and advanced cardiovascular life support exam from the American Heart Association, ChatGPT performed below the 84% passing threshold .
However, its ability to provide detailed rationality with reasonable accuracy makes it a potentially useful tool for self-learning and exam preparation. Mbakwe et al.  argued that the success of ChatGPT in answering medical examinations boils down to the nature of these exams being rote memorization rather than testing analysis and critical thinking.
One of the significantly anticipated applications of chatbots is in the domain of education. AI and technology can be effective in education in several aspects, including personalized learning . In this context, ChatGPT can enhance student participation, provide experiential learning, and help educators in the evaluation of exams and content preparation . Several researchers focused their studies on the impact of ChatGPT in education (, , , ). Potential concerns of ChatGPT in education include response bias ( ), cheating , leakage of private data ( ), and transparency ). Chatbots can also contribute effectively to peer tutoring. Pardos and Bhandari  found that 70% of the hints offered by ChatGPT in elementary and intermediate Algebra topics could result in positive learning gains for students. Frieder et al.  evaluated the mathematical capabilities of ChatGPT in helping Mathematicians with tasks like question answering and finding theorems. The researchers found that ChatGPT displayed a level of Mathematics proficiency below those of an average graduate student.
There is public concern about ChatGPT being used for plagiarism, and it is necessary to create tools to detect such plagiarism. To this end, Khalil and Er  attempted to utilize ChatGPT as a plagiarism detection tool and found that responses created by ChatGPT can often go undetected by other plagiarism checkers. Meanwhile, when ChatGPT was asked to check if the writing was generated by itself, it performed better than other plagiarism detection software. Yang et al.  explored ChatGPT’s ability in summarizing written texts and found that ChatGPT
performs on par with existing fine-tuning methods based on Rouge scores. The authors also highlighted that the current maximum input token length of 5000 is a limitation in assessing ChatGPT’s ability in text summarizing. Can ChatGPT improve student essays? To answer this question, Basic et al.  conducted a study with nine students in the control group and nine in the experimental group that used ChatGPT. The authors concluded that ChatGPT does not necessarily improve essay quality because the control group outperformed the experimental group in most criteria.
Bang et al.  evaluated the effectiveness of ChatGPT on ten reasoning tasks, such as logical reasoning and commonsense reasoning. The authors found that ChatGPT performs reasonably well on deductive reasoning tasks but extremely poor on inductive reasoning. ChatGPT also does well on commonsense reasoning.
For instance, ChatGPT was asked the following question; Please judge if this predicate is (likely) plausible or implausible: “knife chop rock.” To which it answered: It is implausible that a knife could chop through a rock. Knives are made to cut through softer materials such as food, paper, or wood. Rocks are much harder than these materials and a knife would not be able to make a dent in a rock, let alone chop it.
ChatGPT remains a potentially beneficial tool in journalism and misinformation detection, particularly for references and fact-checking. During the COVID-19 pandemic, vaccine misinformation led to vaccine hesitancy in social media . Sallam et al.  provide a descriptive analysis of ChatGPT’s response to vaccine conspiracy statements and compulsory vaccination. The study found that ChatGPT was dismissive of conspiracy statements but provided divided opinions for vaccine mandates by highlighting the pros and cons.
In software development, ChatGPT can be used for tutoring and software testing. Jalil et al.  evaluated ChatGPT’s capabilities in software testing by presenting it with textbook questions. The results are not promising in this case as ChatGPT managed to answer correctly only 37.5% of the answers. Jiao et al.  evaluated ChatGPT’s performance in translation on several benchmark sets. In high-resource European languages, ChatGPT performs on par with commercial applications like Google Translate. However, for low-resource or distant languages, the performance is significantly worse. At this point, let us ask ChatGPT about its potential use in various domains.
Question 3 :
Hi ChatGPT, in what ways can you be used in healthcare, education, and research?
ChatGPT: Hi! As an AI language model, I can be used in various ways in healthcare, education, and research. Here are some examples:
1) Medical Diagnosis and Treatment: AI language models can be trained to analyze medical data such as medical images, electronic health records, and other patient information to assist in medical diagnosis and treatment. This can help Healthcare professionals make more accurate and informed decisions regarding patient care.
2) Patient Interaction: AI language models can be used to help patients interact with healthcare providers and provide them with information about their health conditions, treatment options, and medication instructions.
3) Medical Research: AI language models can help researchers process and analyze large volumes of medical data and identify patterns and trends in disease outbreaks, drug efficacy, and patient outcomes.
1) Language Learning: AI language models can help language learners improve their language proficiency by providing them with personalized feedback, practice exercises, and conversation simulations.
2) Educational Content Creation: AI language models can be used to generate educational content such as quizzes, flashcards, and study guides that are customized to meet the needs of individual learners.
3) Academic Research: AI language models can be used to analyze academic texts and generate summaries, insights, and recommendations that can help researchers gain new insights and discoveries.
1) Data Analysis: AI language models can be used to analyze large volumes of data from various sources and identify patterns, trends, and correlations that are not easily visible to human researchers.
2) Text Mining: AI language models can help researchers extract relevant information from large volumes of unstructured text data such as research papers, news articles, and social media posts.
3) Research Collaboration: AI language models can help researchers collaborate with each other by providing real-time translation services, automated proofreading and editing, and content summarization.
What about journalism and software development?
ChatGPT: Certainly! As an AI language model, I can also be used in journalism and software development in several ways:
1) News Writing and Summarization: AI language models can be used to generate news articles and summaries automatically, saving time and resources for journalists and news organizations.
2) Fact-Checking: AI language models can help fact-check news stories and verify the accuracy of the information presented.
3) Personalization: AI language models can help personalize news content for individual readers based on their interests, reading history, and location.
In Software Development:
1) Chatbots: AI language models can be used to power chatbots, which can interact with users and answer their questions in natural language.
2) Code Generation: AI language models can be used to generate code automatically, reducing the time and effort required for software development.
3) Bug Detection: AI language models can be used to detect bugs and errors in code, improving the quality and reliability of software applications.
Question 5 :
In what other domains can you be used?
ChatGPT: As an AI language model, I can be used in a wide range of domains beyond healthcare, education, research, journalism, and software development. Here are a few examples:
1) Customer Service: AI language models can be used to power chatbots and virtual assistants that can provide customer service and support to users.
2) Financial Services: AI language models can be used to analyze financial data and detect fraud, as well as provide personalized financial advice to individuals.
3) Marketing and Advertising: AI language models can help marketers generate ad copy and content, analyze customer sentiment and behaviour, and personalize marketing campaigns for individual users.
4) Legal Services: AI language models can be used to analyze legal documents and provide legal advice to individuals and businesses.
5) Social Media: AI language models can be used to analyze social media content, monitor online conversations, and detect patterns and trends in user behaviour.
6) Gaming: AI language models can be used to generate dialogue and narrative content for video games, as well as power virtual assistants and chatbots within games.
7) Transportation: AI language models can be used to power voice assistants in vehicles and help drivers navigate and control various vehicle systems.
8) Robotics: AI language models can be used to enable natural language interaction between humans and robots, as well as help robots understand and respond to human commands and requests.
These are just a few examples of how AI language models like mine can be used in various domains.
The possibilities are endless, and new applications are being developed every day as AI technology advances.
ChatGPT has raised a lot of eyebrows for its potential use in writing scientific articles. According to Stokel-Walker , four papers have listed ChatGPT as a co-author for its writing contributions. However, leading publishers like Science have dismissed the possibility of listing an AI chatbot as a co-author while other publishers, including Taylor & Francis, are reviewing their policy when it comes to this issue. Several journals, including the Korean Journal of Radiology, have published policies rejecting any contributions as co-authors from ChatGPT or other AI models .
On the other hand, other journals published guidelines recommending the acknowledgement of any AI tools used for the research but do not allow them to be listed as co-authors (, ). Researchers have also called on journals to clarify what proportion of their papers contain AI-generated content  and publish guidelines for AI use in writing papers . Korinek  explored the potential use cases of language models like ChatGPT for Economic research. The author argues that researchers can be more productive by using language models for tasks like editing and generating headlines. Chen  discussed some of the ethical concerns and potential benefits of using AI tools for scientific writing. The author argued that chatbots can assist writers whose native language is not English.
The paper was written by the author in Chinese and summarized by ChatGPT and translated to English by AI tools. Aydın and Karaarslan  utilized ChatGPT to write a literature review on the role of Digital twins in healthcare. Despite the promising results, the authors found that ChatGPT had significant matches on a plagiarism checker when paraphrasing sentences. Dowling and Lucey  found that ChatGPT is effective in generating plausible research ideas, literature reviews, and testing frameworks.
They also noted that the research quality improves significantly when domain expertise is added as input.
Although ChatGPT can potentially speed up research and writing of scientific papers, there should be human oversight and fact-checking as language models like ChatGPT may generate misleading information (, , , ). Table 1 summarizes the existing works utilizing ChatGPT in several domains.
Limitations of ChatGPT
ChatGPT certainly has the potential for diverse and interesting applications. However, users should consider the limitations of the current model. In this section, we outline some of the current limitations of ChatGPT.
ChatGPT may sound interesting and convincing, but don’t take its word for it! Indeed, ChatGPT’s ability in forming meaningful and conversational sentences is quite impressive, but it may often ‘hallucinate’ responses . Therefore, it is strongly recommended to verify and fact-check any responses from ChatGPT.
ChatGPT makes errors in simple reasoning, logic, mathematics, and presenting factual information .
It is likely that the next version of GPT-4, expected to be released sometime in 2023, will significantly improve ChatGPT. According to several sources, GPT-4 network will be far more complex than its predecessor containing around 100 trillion parameters. The GPT-3 model, in comparison, is made up of 175 billion parameters.
ChatGPT is currently limited in processing a maximum of 5000 tokens of text as input. While this is not a problem in most applications, it can be challenging in tasks like text summarization. Moreover, the current interface of ChatGPT does not allow uploading images or audio files. ChatGPT can produce code representations of visual images based on text prompts, but its drawing skills are somewhat limited currently . In this context, researchers have recently introduced a multi-modal language model trained on multi-modal corpora like image-caption pairs and can perceive general modalities . Multimodale systems can provide applications, such as image generation from text prompts and stem isolation from pop music.
ChatGPT has a tendency to give wordy and detailed responses unless explicitly asked not to. Moreover, ChatGPT expresses fewer emotions than an average human and tends to be more objective . Therefore, ChatGPT cannot replace the need for human connection or be your friend! Similarly, it cannot be used for personal therapy or counseling, which require an intimate human connection.
Although ChatGPT can fetch you information about an incident prior to September 2021, do not expect it to give you the latest news ! When asked about the recent Earthquake in Turkey, ChatGPT replies : ‘I’m sorry, but as an AI language model, I do not have access to information from the future. As of my knowledge cutoff date of September 2021, there had not been any significant earthquakes in Turkey in 2023. However, earthquakes can occur at any time, and it’s important to be prepared and informed about potential risks and safety measures.’
Hi ChatGPT, what are some of your limitations?
ChatGPT: As an AI language model, I have some limitations, some of which include:
1) Limited context: While I can understand and respond to a wide range of topics and questions, my responses are limited to the information and context provided in the question or statement. I do not have the ability to understand context beyond the given information.
2) Lack of emotion and empathy: I do not have emotions, and I cannot empathize with the user or understand their feelings.3) Inability to interpret nonverbal cues: As an AI language model, I do not have the ability to interpret.
3) Inability to interpret nonverbal cues: As an AI language model, I do not have the ability to interpret nonverbal cues, such as body language or facial expressions, which are essential in human communication.
4) Dependence on data quality: My responses are only as good as the data used to train me. If the data used to train me is biased or incomplete, my responses may also be biased or incomplete.
5) Inability to perform physical tasks: I am a language model and do not have a physical body, so I cannot perform physical tasks or interact with the physical world in any way.
Concluding Remarks: Consideration for Ethical and Privacy Concerns.
In this paper, we provided a historical overview of the development of chatbots. In addition, we looked at the significant technological developments that enabled the emergence and success of ChatGPT. We then
described the potential of ChatGPT in several domains and applications. ChatGPT can potentially be used in healthcare for medical screening, answering general questions, and exam preparation. In education,
ChatGPT can be used in tutoring and detecting plagiarism. ChatGPT can also aid researchers with writing, summarizing information, and translating. However, many ethical and privacy concerns need to be addressed about ChatGPT. For instance, some users have reported ChatGPT’s responses containing race and gender bias. Moreover, given its effectiveness, ChatGPT may be used for unethical educational purposes, including cheating. In research, ChatGPT raises ethical questions about copyright and plagiarism. Regarding privacy concerns, ChatGPT is trained with more than 300 billion words, potentially containing internet users’ personal information.
Finally, ChatGPT continues to improve from user interaction, but prompts containing personal information, such as contact, may be processed and even learnt by the model.
 A. M. TURING, “I.—COMPUTING MACHINERY AND INTELLIGENCE,” Mind, vol. LIX, no.236, pp. 433–460, Oct. 1950, doi: 10.1093/mind/LIX.236.433.
 J. Weizenbaum, “ELIZA—a computer program for the study of natural language communication between man and machine,” Commun. ACM, vol. 9, no. 1, pp. 36–45, 1966.
 G. Güzeldere and S. Franchi, “Dialogues with colourful ‘personalities’ of early AI,” Stanf. Humanit.Rev., vol. 4, no. 2, pp. 161–169, 1995.
 A. Pinar Saygin, I. Cicekli, and V. Akman, “Turing Test: 50 Years Later,” Minds Mach., vol. 10, no.4, pp. 463–518, Nov. 2000, doi: 10.1023/A:1011288000451.
 B. Luo, R. Y. K. Lau, C. Li, and Y.-W. Si, “A critical review of state-of-the-art chatbot designs and applications,” WIREs Data Min. Knowl. Discov., vol. 12, no. 1, p. e1434, 2022, doi:10.1002/widm.1434.
 E. Adamopoulou and L. Moussiades, “An Overview of Chatbot Technology,” in Artificial Intelligence Applications and Innovations, Cham, 2020, pp. 373–383. doi: 10.1007/978-3-030- 49186-4_31.
 B. R. Ranoliya, N. Raghuwanshi, and S. Singh, “Chatbot for university-related FAQs,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Sep. 2017, pp. 1525–1530. doi: 10.1109/ICACCI.2017.8126057.
 A. M. Rahman, A. A. Mamun, and A. Islam, “Programming challenges of chatbot: Current and future prospective,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dec. 2017, pp. 75–78. doi: 10.1109/R10-HTC.2017.8288910.
 L. Zhou, J. Gao, D. Li, and H.-Y. Shum, “The Design and Implementation of XiaoIce, an EmpatheticSocial Chatbot,” Comput. Linguist., vol. 46, no. 1, pp. 53–93, Mar. 2020, doi: 10.1162/coli_a_00368.
 A. Følstad and P. B. Brandtzaeg, “Users’ experiences with chatbots: findings from a questionnaire study,” Qual. User Exp., vol. 5, no. 1, p. 3, Apr. 2020, doi: 10.1007/s41233-020-00033-2.
 J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 conference of the North American chapter of the Association for computational linguistics: Human language technologies, volume 1 (long and short papers), 2019, pp. 4171–4186.
 A. Radford et al., “Language models are unsupervised multitask learners,” OpenAI Blog, vol. 1, no.8, p. 9, 2019.
 T. Brown et al., “Language models are few-shot learners,” in Advances in neural information processing systems, 2020, vol. 33, pp. 1877–1901. [Online]. Available:
 H. H. Thorp, “ChatGPT is fun, but not an author,” Science, vol. 379, no. 6630, pp. 313–313, Jan. 2023, doi: 10.1126/science.adg7879.
 H. Else, “Abstracts written by ChatGPT fool scientists,” Nature, vol. 613, no. 7944, pp. 423–423, Jan. 2023, doi: 10.1038/d41586-023-00056-7.
 L. De Angelis et al., “ChatGPT and the Rise of Large Language Models: The New AI-Driven Infodemic Threat in Public Health.” Rochester, NY, Feb. 09, 2023. doi: 10.2139/ssrn.4352931.
 R. A. Khan, M. Jawaid, A. R. Khan, and M. Sajjad, “ChatGPT – Reshaping medical education and clinical management,” Pak. J. Med. Sci., vol. 39, no. 2, Feb. 2023, doi: 10.12669/pjms.39.2.7653.
 A. Rao, J. Kim, M. Kamineni, M. Pang, W. Lie, and M. D. Succi, “Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making.” medRxiv, p. 2023.02.02.23285399, Feb. 07, 2023. doi: 10.1101/2023.02.02.23285399.
 A. T. Gabrielson, A. Y. Odisho, and D. Canes, “Harnessing Generative AI to Improve Efficiency Among Urologists: Welcome ChatGPT,” J. Urol., vol. 0, no. 0, p. 10.1097/JU.0000000000003383, Feb. 2023, doi: 10.1097/JU.0000000000003383.
 A. Hulman et al., “ChatGPT- versus human-generated answers to frequently asked questions about diabetes: a Turing test-inspired survey among employees of a Danish diabetes centre.” medRxiv, p.2023.02.13.23285745, Feb. 15, 2023. doi: 10.1101/2023.02.13.23285745.
 Y. Zhu, D. Han, S. Chen, F. Zeng, and C. Wang, “How Can ChatGPT Benefit Pharmacy: A Case Report on Review Writing.” Preprints, Feb. 20, 2023. doi: 10.20944/preprints202302.0324.v1.
 Y. Shen et al., “ChatGPT and Other Large Language Models Are Double-edged Swords,” Radiology, p. 230163, Jan. 2023, doi: 10.1148/radiol.230163.
 T. H. Kung et al., “Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models,” PLOS Digit. Health, vol. 2, no. 2, p. e0000198, Feb. 2023, doi: 10.1371/journal.pdig.0000198.
 X. Wang et al., “ChatGPT Performs on the Chinese National Medical Licensing Examination.” 2023. doi: 10.21203/rs.3.rs-2584079/v1.
 F. Antaki, S. Touma, D. Milad, J. El-Khoury, and R. Duval, “Evaluating the Performance of ChatGPT in Ophthalmology: An Analysis of its Successes and Shortcomings.” medRxiv, p.2023.01.22.23284882, Jan. 26, 2023. doi: 10.1101/2023.01.22.23284882.
 N. Fijačko, L. Gosak, G. Štiglic, C. T. Picard, and M. J. Douma, “Can ChatGPT pass the life support exams without entering the American heart association course?” Resuscitation, vol. 0, no. 0, Feb. 2023, doi: 10.1016/j.resuscitation.2023.109732.
 A. B. Mbakwe, I. Lourentzou, L. A. Celi, O. J. Mechanic, and A. Dagan, “ChatGPT passing USMLE shines a spotlight on the flaws of medical education,” PLOS Digit. Health, vol. 2, no. 2, p. e0000205, Feb. 2023, doi: 10.1371/journal.pdig.0000205.
 S. Shahriar, J. Ramesh, M. Towheed, T. Ameen, A. Sagahyroon, and A. R. Al-Ali, “Narrative
Integrated Career Exploration Platform,” Front. Educ., vol. 7, 2022, Accessed: Feb. 21, 2023.
[Online]. Available: https://www.frontiersin.org/articles/10.3389/feduc.2022.798950
 E. Kasneci et al., “ChatGPT for Good? On Opportunities and Challenges of Large Language Models
for Education.” EdArXiv, Jan. 29, 2023. doi: 10.35542/osf.io/5er8f.
 J. Rudolph, S. Tan, and S. Tan, “ChatGPT: Bullshit spewer or the end of traditional assessments in
higher education?,” J. Appl. Learn. Teach., vol. 6, no. 1, Art. no. 1, Jan. 2023, doi:
 B. D. Lund and T. Wang, “Chatting about ChatGPT: how may AI and GPT impact academia and
libraries?,” Libr. Hi-Tech News, vol. ahead-of-print, no. ahead-of-print, Jan. 2023, doi:
 D. Mhlanga, “Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong
Learning.” Rochester, NY, Feb. 11, 2023. doi: 10.2139/ssrn.4354422.
 Z. A. Pardos and S. Bhandari, “Learning gain differences between ChatGPT and human tutor
generated algebra hints.” arXiv, Feb. 14, 2023. doi: 10.48550/arXiv.2302.06871.
 S. Frieder et al., “Mathematical Capabilities of ChatGPT.” arXiv, Jan. 31, 2023. doi:
 M. Khalil and E. Er, “Will ChatGPT get you caught? Rethinking of Plagiarism Detection.” arXiv,
Feb. 08, 2023. doi: 10.48550/arXiv.2302.04335.
 X. Yang, Y. Li, X. Zhang, H. Chen, and W. Cheng, “Exploring the Limits of ChatGPT for Query or
Aspect-based Text Summarization.” arXiv, Feb. 15, 2023. doi: 10.48550/arXiv.2302.08081.
 Z. Basic, A. Banovac, I. Kruzic, and I. Jerkovic, “Better by you, better than me, chatgpt3 as writing
assistance in students essays.” arXiv, Feb. 09, 2023. doi: 10.48550/arXiv.2302.04536.
 Y. Bang et al., “A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning,
Hallucination, and Interactivity.” arXiv, Feb. 08, 2023. doi: 10.48550/arXiv.2302.04023.
 K. Hayawi, S. Shahriar, M. A. Serhani, I. Taleb, and S. S. Mathew, “ANTi-Vax: a novel Twitter
dataset for COVID-19 vaccine misinformation detection,” Public Health, vol. 203, pp. 23–30, Feb.
2022, doi: 10.1016/j.puhe.2021.11.022.
 M. Sallam et al., “ChatGPT Output Regarding Compulsory Vaccination and COVID-19 Vaccine
Conspiracy: A Descriptive Study at the Outset of a Paradigm Shift in Online Search for Information,”
Cureus, vol. 15, no. 2, p. e35029, Feb. 2023, doi: 10.7759/cureus.35029.
 S. Jalil, S. Rafi, T. D. LaToza, K. Moran, and W. Lam, “ChatGPT and Software Testing Education:
Promises & Perils.” arXiv, Feb. 08, 2023. doi: 10.48550/arXiv.2302.03287.
 W. Jiao, W. Wang, J. Huang, X. Wang, and Z. Tu, “Is ChatGPT A Good Translator? A Preliminary
Study.” arXiv, Jan. 31, 2023. doi: 10.48550/arXiv.2301.08745.
 C. Stokel-Walker, “ChatGPT listed as an author on research papers: many scientists disapprove,”
Nature, vol. 613, no. 7945, pp. 620–621, Jan. 2023, doi: 10.1038/d41586-023-00107-z.
 S. H. Park, “Authorship Policy of the Korean Journal of Radiology Regarding Artificial Intelligence
Large Language Models Such as ChatGTP,” Korean J. Radiol., vol. 24, no. 3, pp. 171–172, Mar.
2023, doi: 10.3348/kjr.2023.0112.
 J. Thornton, R. D’Souza, and R. Tandon, “Artificial intelligence and psychiatry research and
practice,” Asian J. Psychiatry, p. 103509, Feb. 2023, doi: 10.1016/j.ajp.2023.103509.
 S. Polesie and O. Larkö, “Use of Large Language Models: Editorial Comments,” Acta Derm.
Venereol., vol. 103, p. adv00874, Feb. 2023, doi: 10.2340/actadv.v103.9593.
 G. Tang, “Letter to the editor: Academic journals should clarify the proportion of NLP-generated content
in papers,” Account. Res., vol. 0, no. 0, pp. 1–2, Feb. 2023, doi: 10.1080/08989621.2023.2180359.
 B. Aczel and E.-J. Wagenmakers, “Transparency Guidance for ChatGPT Usage in Scientific
Writing.” PsyArXiv, Feb. 06, 2023. doi: 10.31234/osf.io/b58ex.
 A. Korinek, “Language Models and Cognitive Automation for Economic Research.” National Bureau
of Economic Research, Feb. 2023. doi: 10.3386/w30957.
 T.-J. Chen, “ChatGPT and other artificial intelligence applications speed up scientific writing,” J.
Chin. Med. Assoc., p. 10.1097/JCMA.0000000000000900, doi: 10.1097/JCMA.0000000000000900.
 Ö. Aydın and E. Karaarslan, “OpenAI ChatGPT Generated Literature Review: Digital Twin in
Healthcare.” Rochester, NY, Dec. 21, 2022. doi: 10.2139/ssrn.4308687.
 M. Dowling and B. Lucey, “ChatGPT for (Finance) research: The Bananarama Conjecture,” Finance
Res. Lett., p. 103662, Jan. 2023, doi: 10.1016/j.frl.2023.103662.
 C. Stokel-Walker and R. Van Noorden, “What ChatGPT and generative AI mean for science,”
Nature, vol. 614, no. 7947, pp. 214–216, Feb. 2023, doi: 10.1038/d41586-023-00340-6.
 J. Y. Lee, “Can an artificial intelligence chatbot be the author of a scholarly article?,” Sci. Ed., vol.
10, no. 1, pp. 7–12, Feb. 2023, doi: https://doi.org/10.6087/kcse.292.
 Z. Lin, “Why and how to embrace AI such as ChatGPT in your academic life.” PsyArXiv, Feb. 05,
 H. Alkaissi and S. I. McFarlane, “Artificial Hallucinations in ChatGPT: Implications in Scientific
Writing,” Cureus, vol. 15, no. 2, p. e35179, Feb. 2023, doi: 10.7759/cureus.35179.
 A. Borji, “A Categorical Archive of ChatGPT Failures.” arXiv, Feb. 21, 2023. doi:
 S. Huang et al., “Language Is Not All You Need: Aligning Perception with Language Models.”
arXiv, Mar. 01, 2023. doi: 10.48550/arXiv.2302.14045.
 B. Guo et al., “How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection.” arXiv, Jan. 18, 2023.