Evaluating the use of large language model in identifying top research questions in gastroenterology

Lahat, Adi; Shachar, Eyal; Avidan, Benjamin; Shatz, Zina; Glicksberg, Benjamin S.; Klang, Eyal

doi:10.1038/s41598-023-31412-2

Cited by 71 publications

(45 citation statements)

References 13 publications

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“…The version column in Table 2 shows the version of ChatGPT used for evaluation. [63] found that newer versions of ChatGPT tend to have better performance on the same questions. In contrast to using ChatGPT directly for writing, which is expressly banned by many scientific journals, exploring new research ideas/topics with the assistance of ChatGPT faces less ethical issues.…”

Section: Reviews Of Level 3 Papersmentioning

confidence: 91%

“…A few studies evaluate the use of ChatGPT in medical research, particularly in scientific writing [67] and generating research questions [63] and systematic review topics [38]. In [67], the authors use ChatGPT to generate full abstracts, providing only the title and result sections of the abstracts from 50 real scientific publications.…”

Section: Reviews Of Level 3 Papersmentioning

confidence: 99%

“…In [38], ChatGPT is used to generate systematic review topics in plastic surgery. Similar to [63], ChatGPT-generated research topics are generally not novel. The version column in Table 2 shows the version of ChatGPT used for evaluation.…”

Section: Reviews Of Level 3 Papersmentioning

confidence: 99%

“…The finding indicates that Grammarly could potentially be adopted by scientific journals to enforce the 'no-AI-generated-texts' policy. In [63], the authors use ChatGPT to identify research questions in gastroenterology. The answers generated by Chat-GPT proves to be highly relevant but lack depth and novelty.…”

mentioning

confidence: 99%

“…In contrast to using ChatGPT directly for writing, which is expressly banned by many scientific journals, exploring new research ideas/topics with the assistance of ChatGPT faces less ethical issues. However, [63,38] demonstrated that the current version of ChatGPT is not sufficiently qualified for such tasks. Humans still play dominating roles in ingenious and innovative research.…”

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confidence: 99%

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ChatGPT in Healthcare: A Taxonomy and Systematic Review

Li¹,

Amin²,

Kleesiek³

et al. 2023

Preprint

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

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Section: Reviews Of Level 3 Papersmentioning

confidence: 91%

Section: Reviews Of Level 3 Papersmentioning

confidence: 99%

Section: Reviews Of Level 3 Papersmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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ChatGPT in Healthcare: A Taxonomy and Systematic Review

Li¹,

Amin²,

Kleesiek³

et al. 2023

Preprint

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Large Language Model‐Based Chatbots in Higher Education

Yigci,

Eryilmaz,

Yetisen

et al. 2024

Advanced Intelligent Systems

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Large language models (LLMs) are artificial intelligence (AI) platforms capable of analyzing and mimicking natural language processing. Leveraging deep learning, LLM capabilities have been advanced significantly, giving rise to generative chatbots such as Generative Pre‐trained Transformer (GPT). GPT‐1 was initially released by OpenAI in 2018. ChatGPT's release in 2022 marked a global record of speed in technology uptake, attracting more than 100 million users in two months. Consequently, the utility of LLMs in fields including engineering, healthcare, and education has been explored. The potential of LLM‐based chatbots in higher education has sparked significant interest and ignited debates. LLMs can offer personalized learning experiences and advance asynchronized learning, potentially revolutionizing higher education, but can also undermine academic integrity. Although concerns regarding AI‐generated output accuracy, the spread of misinformation, propagation of biases, and other legal and ethical issues have not been fully addressed yet, several strategies have been implemented to mitigate these limitations. Here, the development of LLMs, properties of LLM‐based chatbots, and potential applications of LLM‐based chatbots in higher education are discussed. Current challenges and concerns associated with AI‐based learning platforms are outlined. The potentials of LLM‐based chatbot use in the context of learning experiences in higher education settings are explored.

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An Exploration of the Four‐Decade‐Long History of Photocatalytic Water Treatment: Analysis of Key Advancements, Underlying Relationships, and Failures along the Way

Ansari,

Moussavi,

Samoili

et al. 2023

Solar RRL

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This study explores the decade‐long history of photocatalytic water treatment using a novel integration of the Delphi method and the preferred reporting items for systematic reviews and meta‐analyses approach, along with bibliometric analysis, to examine publication trends, author and country coauthorship networks, keyword co‐occurrence, citation analysis, and catalyst/contaminant evolution over time. The key findings include a steady increase in annual publications, a shift in dominant disciplines toward Environmental Sciences and Chemical Engineering, the role of specific authors in country productivity and citation rankings, and a growing need for international collaboration. TiO2‐ and TiO2‐based materials remain dominant, but a clear trend of abandonment in favor of novel materials is observed. Solar light is gaining importance for the photocatalytic elimination of aquatic pollutants, shifting simple organic compounds to priority substances. The evolution of this field is highlighted by tracing advancements in catalyst diversity and complexity, process optimization, and intensification. However, despite important milestone achievements, persistent knowledge gaps, discrepancies in comparability among studies, a lack of understanding of the fundamentals, and low funding incentives for transitioning to market hinder large‐scale applications. This study aims to provide a comprehensive guide for researchers by summarizing previous findings, unsuccessful approaches, and possibilities for improving photocatalytic applications.

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Evaluating the use of large language model in identifying top research questions in gastroenterology

Cited by 71 publications

References 13 publications

ChatGPT in Healthcare: A Taxonomy and Systematic Review

ChatGPT in Healthcare: A Taxonomy and Systematic Review

Large Language Model‐Based Chatbots in Higher Education

An Exploration of the Four‐Decade‐Long History of Photocatalytic Water Treatment: Analysis of Key Advancements, Underlying Relationships, and Failures along the Way

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