Matches in SemOpenAlex for { <https://semopenalex.org/work/W4327546908> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W4327546908 abstract "<sec> <title>UNSTRUCTURED</title> The recent massive success of ChatGTP, a generative language model (also known as generative language model) developed by OpenAI, has spurred the development of other similar models. Many are impressed by the chatbot’s ability to generate human-like responses on diverse topics, through state-of-the-art natural language processing model developed using Reinforcement Learning with Human Feedback (RLHF). Over the years, researchers have explored the use of generative language model in various aspects of clinical medicine, even before the release of ChatGPT. The emergence of powerful applications like ChatGPT makes the use of artificial intelligence (AI) promising in clinical medicine. However, despite the encouraging outlook, the use of ChatGPT and other AI applications is not without challenges. This article offers an in-depth overview of generative language model such as ChatGPT and discusses the pros and cons and its ethical considerations in clinical medicine. </sec>" @default.
- W4327546908 created "2023-03-17" @default.
- W4327546908 creator A5004136927 @default.
- W4327546908 creator A5015125630 @default.
- W4327546908 creator A5018456400 @default.
- W4327546908 date "2023-03-14" @default.
- W4327546908 modified "2023-09-27" @default.
- W4327546908 title "Generative Language Model in Clinical Medicine: Opportunities and Drawback (Preprint)" @default.
- W4327546908 cites W2412799112 @default.
- W4327546908 cites W3134818558 @default.
- W4327546908 cites W3136783667 @default.
- W4327546908 cites W3153608027 @default.
- W4327546908 cites W3200759624 @default.
- W4327546908 cites W3211135240 @default.
- W4327546908 cites W4200465893 @default.
- W4327546908 cites W4210523443 @default.
- W4327546908 cites W4308179942 @default.
- W4327546908 cites W4315648146 @default.
- W4327546908 cites W4315784554 @default.
- W4327546908 cites W4316671929 @default.
- W4327546908 cites W4317390716 @default.
- W4327546908 cites W4318591734 @default.
- W4327546908 cites W4319460874 @default.
- W4327546908 cites W4319656294 @default.
- W4327546908 cites W4319663047 @default.
- W4327546908 cites W4319868628 @default.
- W4327546908 cites W4320186815 @default.
- W4327546908 cites W4320486107 @default.
- W4327546908 doi "https://doi.org/10.2196/preprints.47274" @default.
- W4327546908 hasPublicationYear "2023" @default.
- W4327546908 type Work @default.
- W4327546908 citedByCount "0" @default.
- W4327546908 crossrefType "posted-content" @default.
- W4327546908 hasAuthorship W4327546908A5004136927 @default.
- W4327546908 hasAuthorship W4327546908A5015125630 @default.
- W4327546908 hasAuthorship W4327546908A5018456400 @default.
- W4327546908 hasConcept C136764020 @default.
- W4327546908 hasConcept C137293760 @default.
- W4327546908 hasConcept C154945302 @default.
- W4327546908 hasConcept C15744967 @default.
- W4327546908 hasConcept C167966045 @default.
- W4327546908 hasConcept C188147891 @default.
- W4327546908 hasConcept C2779041454 @default.
- W4327546908 hasConcept C39890363 @default.
- W4327546908 hasConcept C41008148 @default.
- W4327546908 hasConcept C43169469 @default.
- W4327546908 hasConceptScore W4327546908C136764020 @default.
- W4327546908 hasConceptScore W4327546908C137293760 @default.
- W4327546908 hasConceptScore W4327546908C154945302 @default.
- W4327546908 hasConceptScore W4327546908C15744967 @default.
- W4327546908 hasConceptScore W4327546908C167966045 @default.
- W4327546908 hasConceptScore W4327546908C188147891 @default.
- W4327546908 hasConceptScore W4327546908C2779041454 @default.
- W4327546908 hasConceptScore W4327546908C39890363 @default.
- W4327546908 hasConceptScore W4327546908C41008148 @default.
- W4327546908 hasConceptScore W4327546908C43169469 @default.
- W4327546908 hasLocation W43275469081 @default.
- W4327546908 hasOpenAccess W4327546908 @default.
- W4327546908 hasPrimaryLocation W43275469081 @default.
- W4327546908 hasRelatedWork W1982082392 @default.
- W4327546908 hasRelatedWork W1992580977 @default.
- W4327546908 hasRelatedWork W2981955290 @default.
- W4327546908 hasRelatedWork W3003214776 @default.
- W4327546908 hasRelatedWork W3017062960 @default.
- W4327546908 hasRelatedWork W3107474891 @default.
- W4327546908 hasRelatedWork W3215252950 @default.
- W4327546908 hasRelatedWork W4309397537 @default.
- W4327546908 hasRelatedWork W4327546908 @default.
- W4327546908 hasRelatedWork W4352976785 @default.
- W4327546908 isParatext "false" @default.
- W4327546908 isRetracted "false" @default.
- W4327546908 workType "article" @default.