iScience, Год журнала: 2023, Номер 26(11), С. 108163 - 108163
Опубликована: Окт. 10, 2023
Язык: Английский
Healthcare, Год журнала: 2023, Номер 11(6), С. 887 - 887
Опубликована: Март 19, 2023
ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined addressed. current systematic review aimed to investigate utility highlight its limitations. Using PRIMSA guidelines, a search was conducted retrieve English records PubMed/MEDLINE Google Scholar (published research or preprints) that context practice. A total 60 were eligible for inclusion. Benefits cited 51/60 (85.0%) included: (1) improved scientific writing enhancing equity versatility; (2) (efficient analysis datasets, code generation, literature reviews, saving time focus on experimental design, drug discovery development); (3) benefits (streamlining workflow, cost saving, documentation, personalized medicine, literacy); (4) education including learning critical thinking problem-based learning. Concerns regarding use stated 58/60 (96.7%) ethical, copyright, transparency, legal issues, risk bias, plagiarism, lack originality, inaccurate content with hallucination, limited knowledge, incorrect citations, cybersecurity infodemics. can induce paradigm shifts However, embrace this AI chatbot should extreme caution considering As it currently stands, does not qualify listed as author articles unless ICMJE/COPE guidelines revised amended. An initiative involving all stakeholders urgently needed. This will help set ethics guide responsible among other academia.
Язык: Английский
Процитировано
1813
Narra J, Год журнала: 2023, Номер 3(1), С. e103 - e103
Опубликована: Март 29, 2023
Since its public release in November 2022, ChatGPT has gained a widespread attention and received mixed responses the academia. Promising applications of university education been suggested; however, several concerns were raised. The aim this descriptive study was to investigate pros cons use medical, dental, pharmacy, health education. Based on expert panel discussion review existing literature, specific concise prompts constructed generated 25 February 2023. Out data suggested that medical education, benefits included possibility improving personalized learning, clinical reasoning understanding complex concepts. listed context dental improved skills through step-by-step instructions interactive content, with instant feedback student techniques. In pharmacy advantages possible explanations subjects deployment tools aiding develop for patient counselling. providing case scenarios, besides analysis literature review. limitations based ChatGPT-generated content common across all investigated healthcare disciplines privacy issues, risk generating biased inaccurate deterioration critical thinking communication among students. deemed partially helpful by panel. However, important points regarding missed including: plagiarism, copyright academic dishonesty, lack personal emotional interactions necessary developing proper conclusion, despite promising prospects drawbacks should be addressed implementation guidelines ensure exploiting innovative technology.
Язык: Английский
Процитировано
193
EBioMedicine, Год журнала: 2023, Номер 95, С. 104770 - 104770
Опубликована: Авг. 23, 2023
Язык: Английский
Процитировано
191
Heliyon, Год журнала: 2023, Номер 9(11), С. e20962 - e20962
Опубликована: Окт. 18, 2023
Open AI's ChatGPT has emerged as a popular AI language model that can engage in natural conversations with users. Based on qualitative research approach using semistructured interviews 32 users from India, this study examined the factors influencing users' acceptance and use of unified theory usage technology (UTAUT) model. The results demonstrated four UTAUT, along two extended constructs, i.e. perceived interactivity privacy concerns, explain interaction engagement ChatGPT. also found age experience moderate impact various theoretical practical implications were discussed.
Язык: Английский
Процитировано
143
Clinical Medicine, Год журнала: 2023, Номер 23(3), С. 278 - 279
Опубликована: Апрель 21, 2023
ChatGPT, which can automatically generate written responses to queries using internet sources, soon went viral after its release at the end of 2022. The performance ChatGPT on medical exams shows results near passing threshold, making it comparable third-year students. It also write academic abstracts or reviews an acceptable level. However, is not clear how deals with harmful content, misinformation plagiarism; therefore, authors professionally for writing should be cautious. has potential facilitate interaction between healthcare providers and patients in various ways. sophisticated tasks such as understanding human anatomy are still a limitation ChatGPT. simplify radiological reports, but possibility incorrect statements missing information remain. Although change practice, education research, further improvements this application needed regular use medicine.
Язык: Английский
Процитировано
136
JMIR Medical Education, Год журнала: 2023, Номер 9, С. e48785 - e48785
Опубликована: Сен. 28, 2023
Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT Bard, can generate new content have a wide range of possible applications.
Язык: Английский
Процитировано
130
Natural Language Processing Journal, Год журнала: 2023, Номер 6, С. 100048 - 100048
Опубликована: Дек. 19, 2023
Large language models (LLMs) are a special class of pretrained (PLMs) obtained by scaling model size, pretraining corpus and computation. LLMs, because their large size on volumes text data, exhibit abilities which allow them to achieve remarkable performances without any task-specific training in many the natural processing tasks. The era LLMs started with OpenAI's GPT-3 model, popularity has increased exponentially after introduction like ChatGPT GPT4. We refer its successor OpenAI models, including GPT4, as family (GLLMs). With ever-rising GLLMs, especially research community, there is strong need for comprehensive survey summarizes recent progress multiple dimensions can guide community insightful future directions. start paper foundation concepts transformers, transfer learning, self-supervised models. then present brief overview GLLMs discuss various downstream tasks, specific domains languages. also data labelling augmentation robustness effectiveness evaluators, finally, conclude To summarize, this will serve good resource both academic industry people stay updated latest related GLLMs.
Язык: Английский
Процитировано
122
Information, Год журнала: 2023, Номер 14(8), С. 462 - 462
Опубликована: Авг. 16, 2023
This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is revolutionizing generative text. We provide comprehensive analysis its architecture, training data, and evaluation metrics explore advancements enhancements over time. Additionally, we examine the capabilities limitations ChatGPT in natural processing (NLP) tasks, including translation, text summarization, dialogue generation. Furthermore, compare to other generation models discuss applicability various tasks. Our also addresses ethical privacy considerations associated with provides insights into mitigation strategies. Moreover, investigate role cyberattacks, highlighting potential security risks. Lastly, showcase diverse applications different industries evaluate performance across languages domains. offers exploration ChatGPT’s impact on NLP field.
Язык: Английский
Процитировано
111
Journal of the American Medical Informatics Association, Год журнала: 2024, Номер 31(9), С. 1812 - 1820
Опубликована: Янв. 27, 2024
Abstract Importance The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data extracting meaningful information with minimal training data. By developing refining prompt-based strategies, we can significantly enhance models’ performance, making them viable tools for NER tasks possibly reducing reliance on extensive annotated datasets. Objectives This quantifies capabilities GPT-4 named entity recognition (NER) proposes task-specific prompts to improve their performance. Materials Methods We evaluated these models 2 tasks: (1) extract medical problems, treatments, tests from notes MTSamples corpus, following 2010 i2b2 concept extraction shared task, (2) identify nervous system disorder-related adverse events safety reports vaccine event reporting (VAERS). To GPT models' developed a prompt framework that includes baseline task description format specification, annotation guideline-based prompts, (3) error analysis-based instructions, (4) samples few-shot learning. assessed each prompt's effectiveness compared BioClinicalBERT. Results Using achieved relaxed F1 scores 0.634, 0.804 0.301, 0.593 VAERS. Additional components consistently improved model When all 4 were used, socres 0.794, 0.861 0.676, 0.736 VAERS, demonstrating our framework. Although results trail BioClinicalBERT (F1 0.901 dataset 0.802 VAERS), it is very promising considering few are needed. Discussion study’s findings suggest direction leveraging LLMs tasks. However, while performance there's need further development refinement. like show achieving close state-of-the-art BioClinicalBERT, but they still require careful engineering understanding knowledge. also underscores importance evaluation schemas accurately reflect settings. Conclusion While direct application falls short optimal framework, incorporating knowledge samples, enhances feasibility applications.
Язык: Английский
Процитировано
106
JMIR Medical Education, Год журнала: 2023, Номер 9, С. e48254 - e48254
Опубликована: Авг. 14, 2023
ChatGPT is a conversational large language model that has the potential to revolutionize knowledge acquisition. However, impact of this technology on quality education still unknown considering risks and concerns surrounding use. Therefore, it necessary assess usability acceptability promising tool. As an innovative technology, intention use can be studied in context acceptance (TAM).
Язык: Английский
Процитировано
105