Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including ...passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society.
•Micronucleus (MN) tests were integrated into a transgenic rat gene mutation assay.•gpt delta rats were orally administered benzoapyrene (BaP) for 28 days.•BaP induced gene mutation in bone marrow, ...liver, and colon.•BaP induced MN in bone marrow and peripheral blood, but not in liver or colon.•No remarkable difference was observed in mutant frequencies between sampling times.
Reduction of the number of animals used in in vivo genotoxicity tests is encouraged. For this purpose, we conducted integrated toxicity tests combining gene mutation assays with multiple-organ micronucleus (MN) tests (peripheral blood, bone marrow, liver, and colon) in F344 gpt delta transgenic (Tg) rats. Seven-week-old male F344 gpt delta rats were orally administered 62.5 or 125 mg/kg/day benzoapyrene (BaP) for 28 days. One day after the final day of treatment (day 29) and three days after the final treatment (day 31), bone marrow, liver, and colon samples were collected, and mutation assays and MN tests were performed. The gpt mutant frequency (MF) significantly increased in bone marrow, liver and colon but MN induction was only significant in bone marrow but not in liver and colon. Similarly MN induction was only observed in bone marrow in non-Tg F344 rats. In peripheral blood obtained on day 4, 15, 29, 31, a time-dependent increase was observed in reticulocyte MN frequency during the treatment. Thus, our integrated method successfully detected both gene mutations and MN induction caused by BaP. In addition, no significant differences were observed between sampling times (day 29 versus 31), suggesting that sampling on day 29 is also valid to evaluate gene mutations. On the other hand, MN results in bone marrow and peripheral blood were different depending on the sampling day. An appropriate sampling day should be designated according to which assays are integrated. We confirmed that integration of the MN test with a gene mutation assay using F344 gpt delta Tg rats is useful to evaluate different endpoints related to genotoxicity using the same animals and to reduce animal use.
Digital technology has been understood as a General Purpose Technology (GPT) given its systemic and pervasive nature, and heralded as key to sustainability transitions. We perform a scoping review of ...112 contributions to critically appraise research on the sustainability effects of contemporary digitalization. We find that many studies adopt a rather reductionist, deterministic and optimistic lens on the (potential) sustainability effects of digital technologies, mostly neglecting the systemic effects inherent to GPTs. For a better understanding of systemic sustainability effects of contemporary digitalization, we advocate the use of exploratory designs and prospective methods, and a theoretical understanding of technologies as co-evolving with institutions and practices.
•We review 112 articles across academic fields and digitalization topics.•In our sample, reductionism, determinism and optimism hamper a systemic approach.•We propose avenues to study systemic sustainability effects of digitalization.•Future research would benefit from multidisciplinary and exploratory approaches.•Examining the social construction of technologies can help avoid unfounded optimism.
The ability of Large Language Models (LLMs) to analyze and respond to freely written text is causing increasing excitement in the field of psychiatry; the application of such models presents unique ...opportunities and challenges for psychiatric applications. This review article seeks to offer a comprehensive overview of LLMs in psychiatry, their model architecture, potential use cases, and clinical considerations. LLM frameworks such as ChatGPT/GPT-4 are trained on huge amounts of text data that are sometimes fine-tuned for specific tasks. This opens up a wide range of possible psychiatric applications, such as accurately predicting individual patient risk factors for specific disorders, engaging in therapeutic intervention, and analyzing therapeutic material, to name a few. However, adoption in the psychiatric setting presents many challenges, including inherent limitations and biases in LLMs, concerns about explainability and privacy, and the potential damage resulting from produced misinformation. This review covers potential opportunities and limitations and highlights potential considerations when these models are applied in a real-world psychiatric context.
The creation of chatbots, such as Generative Pre-trained Transformer (GPT), is a result of recent developments in natural language processing (NLP). Even though Chat GPT has demonstrated enormous ...promise in a number of areas, including scientific research, this impact is still developing. This paper attempts to investigate the possibilities, threats, limits, and ethical issues surrounding Chat GPT in scientific research. The assessment of the literature on Chat GPT and scientific research is followed by the presentation of case examples that demonstrate the potential advantages and difficulties of Chat GPT use in scientific research. Finally, we conclude by pointing about the ethical issues that need to be tackled before Chat GPT can be completely utilized in scientific research.
Background: Hepatitis B is the main infection of the injured liver for humans. Inflammation of the liver is caused by hepatitis viruses may lead to cirrhosis and hepatocellular carcinoma. The B-cell ...stimulatory factor 2 (BSF-2) is one of the cytokines that affect the regulation and differentiation of the human immune response. Objective: this report aims to estimate the BSF-2, GPT, and GOT levels in patients’ serum with different stages of hepatitis B compared with healthy control. Methods: This study assessed 52 patients presumably with acute and chronic cases who have HBsAg positive. BSF-2 was detected using ELISA assay. Biochemical parameters were determined using kits of an automated analyzer. SPSS version-16 software was used for statistical analysis. Results: Acute hepatitis B patients had shown elevation in BSF-2 level more than of chronic hepatitis B. GPT and GOT levels elevated in the acute hepatitis group more than of the chronic hepatitis group. We reported a significant value between BSF-2, GOT, and GPT levels. We didn’t score an association between patient’s age and cases groups of hepatitis. Conclusion: our data confirmed increasing of BSF-2 levels with the increase of GOT level more than GPT level with acute hepatitis B. BSF-2, GPT and GOT levels are varied in different courses of acute and chronic HBV. We surmised that the elevation of BSF-2 levels designates liver injury of patients with acute HBV.
The use of transformer‐based language models in artificial intelligence (AI) has increased adoption in various industries and led to significant productivity advancements in business operations. This ...article explores how these models can be used to augment human innovation teams in the new product development process, allowing for larger problem and solution spaces to be explored and ultimately leading to higher innovation performance. The article proposes the use of the AI‐augmented double diamond framework to structure the exploration of how these models can assist in new product development (NPD) tasks, such as text summarization, sentiment analysis, and idea generation. It also discusses the limitations of the technology and the potential impact of AI on established practices in NPD. The article establishes a research agenda for exploring the use of language models in this area and the role of humans in hybrid innovation teams. (Note: Following the idea of this article, GPT‐3 alone generated this abstract. Only minor formatting edits were performed by humans.)
Since their introduction, function calls have become a widely used feature within the OpenAI API ecosystem. They reliably connect GPT’s capabilities with external tools and APIs, and they quickly ...found their way into the other LLMs. The challenge one can encounter is the number of tokens consumed per request since each definition of each function is always sent as an input. We propose a simple solution to effectively decrease the number of tokens by sending only the function corresponding to the user question. The solution is based on saving the functions to the vector database, where we use the similarity score to pick only the functions that need to be sent. We have benchmarked that our solution can decrease the average prompt token consumption by 210% and the average prompt (input) price by 244% vs the default function call. Our solution is not limited to specific LLMs. It can be integrated with any LLM that supports function calls, making it a versatile tool for reducing token consumption. This means that even cheaper models with a high volume of functions can benefit from our solution.
Should consumer researchers employ silicon samples and artificially generated data based on large language models, such as GPT, to mimic human respondents' behavior? In this paper, we review recent ...research that has compared result patterns from silicon and human samples, finding that results vary considerably across different domains. Based on these results, we present specific recommendations for silicon sample use in consumer and marketing research. We argue that silicon samples hold particular promise in upstream parts of the research process such as qualitative pretesting and pilot studies, where researchers collect external information to safeguard follow‐up design choices. We also provide a critical assessment and recommendations for using silicon samples in main studies. Finally, we discuss ethical issues of silicon sample use and present future research avenues.
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