New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology

Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model cap...

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Published in:	British journal of ophthalmology Vol. 106; no. 7; pp. 889 - 892
Main Authors:	Nath, Siddharth, Marie, Abdullah, Ellershaw, Simon, Korot, Edward, Keane, Pearse A
Format:	Journal Article
Language:	English
Published:	BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.07.2022 BMJ Publishing Group LTD
Subjects:	Cataracts Datasets Diagnostic tests/Investigation Electronic health records Gender Humans Internet Language instruction Medical diagnosis Medical personnel Medical research Natural Language Processing Ophthalmology Pain management Patients Review Telemedicine Telemedicine Diagnostic tests/Investigation
ISSN:	0007-1161, 1468-2079, 1468-2079
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Abstract	Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.
AbstractList	Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care. Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.
Author	Ellershaw, Simon Marie, Abdullah Keane, Pearse A Nath, Siddharth Korot, Edward
Author_xml	– sequence: 1 givenname: Siddharth orcidid: 0000-0003-4520-7163 surname: Nath fullname: Nath, Siddharth organization: National Institute for Health Research, Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology, Moorfields Eye Hospital City Road Campus, London, UK – sequence: 2 givenname: Abdullah surname: Marie fullname: Marie, Abdullah organization: School of Medicine and Dentistry, Queen's University Belfast, Belfast, UK – sequence: 3 givenname: Simon surname: Ellershaw fullname: Ellershaw, Simon organization: UKRI Centre for Doctoral Training in AI-enabled Healthcare, University College London, London, UK – sequence: 4 givenname: Edward orcidid: 0000-0002-5687-1564 surname: Korot fullname: Korot, Edward organization: Byers Eye Institute, Stanford University, Stanford, California, USA – sequence: 5 givenname: Pearse A surname: Keane fullname: Keane, Pearse A email: p.keane@ucl.ac.uk organization: National Institute for Health Research, Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology, Moorfields Eye Hospital City Road Campus, London, UK
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Cites_doi	10.1126/science.aal4230 10.1111/j.1525-1497.2006.00415.x 10.1001/jamaophthalmol.2018.7051 10.1016/j.jbi.2021.103864 10.1111/ceo.13666 10.1097/ICU.0000000000000789 10.1002/pds.4149 10.1016/S0140-6736(21)00722-4 10.1038/s41746-021-00464-x 10.22148/001c.17212 10.1136/amiajnl-2011-000456 10.1016/S2589-7500(19)30108-6 10.1007/s11999-011-1770-9 10.1007/s11023-020-09548-1 10.1111/ceo.13340 10.1111/j.1526-4610.2010.01763.x 10.1200/JCO.2017.35.15_suppl.6501 10.1016/j.ophtha.2020.10.012 10.5301/ejo.5000706 10.1007/s11136-008-9399-1 10.1177/0840470419872775 10.1109/PROC.1986.13580 10.1093/oso/9780198250791.003.0017 10.1109/MICC.2017.8311752 10.3115/v1/D14-1179
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References	Brown, Mann, Ryder (R6) 2020 Gaskin, Pershing, Cole (R27) 2016; 26 Naseri, Kafi, Skamene (R14) 2021; 120 Liu, Shorstein, Amsden (R28) 2017; 26 Caliskan, Bryson, Narayanan (R12) 2017; 356 Stein, Rahman, Andrews (R24) 2019; 137 Elkins, Chun (R3) 2020; 5 Tan, Bacchi, Casson (R25) 2020; 48 Turing (R2) 2004 Wu, Chen, Yun (R29) 2021; 128 Cho, van Merrienboer, Gulcehre (R8) 2014 Korngiebel, Mooney (R13) 2021; 4 Floridi, Chiriatti (R5) 2020; 30 Smith, Johnson, Russell (R26) 2008; 17 Radford, Kim, Hallacy (R34) 2021 Guida, Mauri (R1) 1986; 74 Zheng, Luo, Mercado (R23) 2019; 47 Faes, Wagner, Fu (R31) 2019; 1 Mularski, White-Chu, Overbay (R19) 2006; 21 Keane, Topol (R32) 2021; 397 Ramesh, Pavlov, Goh (R35) 2021 Yang, Ng, Foo (R20) 2021; 32 Peissig, Rasmussen, Berg (R21) 2012; 19 Beck, Vinegra, Dankwa-Mullan (R15) 2017; 35 Coghill (R18) 2010; 50 Mossey (R17) 2011; 469 Crampton (R30) 2020; 33 2023062822101722000_106.7.889.15 Liu (2023062822101722000_106.7.889.28) 2017; 26 2023062822101722000_106.7.889.16 2023062822101722000_106.7.889.35 2023062822101722000_106.7.889.36 2023062822101722000_106.7.889.11 2023062822101722000_106.7.889.33 2023062822101722000_106.7.889.12 2023062822101722000_106.7.889.34 2023062822101722000_106.7.889.10 Tan (2023062822101722000_106.7.889.25) 2020; 48 2023062822101722000_106.7.889.19 2023062822101722000_106.7.889.17 2023062822101722000_106.7.889.18 Guida (2023062822101722000_106.7.889.1) 1986; 74 Korngiebel (2023062822101722000_106.7.889.13) 2021; 4 Naseri (2023062822101722000_106.7.889.14) 2021; 120 2023062822101722000_106.7.889.26 2023062822101722000_106.7.889.21 Yang (2023062822101722000_106.7.889.20) 2021; 32 Crampton (2023062822101722000_106.7.889.30) 2020; 33 2023062822101722000_106.7.889.29 2023062822101722000_106.7.889.3 Faes (2023062822101722000_106.7.889.31) 2019; 1 2023062822101722000_106.7.889.2 2023062822101722000_106.7.889.5 2023062822101722000_106.7.889.4 2023062822101722000_106.7.889.7 2023062822101722000_106.7.889.6 2023062822101722000_106.7.889.9 Stein (2023062822101722000_106.7.889.24) 2019; 137 2023062822101722000_106.7.889.8 Barrows Jr (2023062822101722000_106.7.889.22) 2000 Zheng (2023062822101722000_106.7.889.23) 2019; 47 Gaskin (2023062822101722000_106.7.889.27) 2016; 26 Keane (2023062822101722000_106.7.889.32) 2021; 397
References_xml	– year: 2021 ident: R35 article-title: Zero-Shot Text-to-Image Generation [Internet] publication-title: arXiv – volume: 356 start-page: 183 year: 2017 ident: R12 article-title: Semantics derived automatically from language corpora contain human-like biases publication-title: Science doi: 10.1126/science.aal4230 – volume: 21 start-page: 607 year: 2006 ident: R19 article-title: Measuring pain as the 5th vital sign does not improve quality of pain management publication-title: J Gen Intern Med doi: 10.1111/j.1525-1497.2006.00415.x – volume: 137 start-page: 491 year: 2019 ident: R24 article-title: Evaluation of an algorithm for identifying ocular conditions in electronic health record data publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2018.7051 – volume: 120 year: 2021 ident: R14 article-title: Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases publication-title: J Biomed Inform doi: 10.1016/j.jbi.2021.103864 – year: 2004 ident: R2 article-title: Computing Machinery and Intelligence (1950) [Internet] publication-title: The Essential Turing – volume: 48 start-page: 169 year: 2020 ident: R25 article-title: Triaging ophthalmology outpatient referrals with machine learning: a pilot study publication-title: Clin Exp Ophthalmol doi: 10.1111/ceo.13666 – volume: 32 start-page: 397 year: 2021 ident: R20 article-title: Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions publication-title: Curr Opin Ophthalmol doi: 10.1097/ICU.0000000000000789 – volume: 26 start-page: 378 year: 2017 ident: R28 article-title: Natural language processing to ascertain two key variables from operative reports in ophthalmology publication-title: Pharmacoepidemiol Drug Saf doi: 10.1002/pds.4149 – volume: 397 year: 2021 ident: R32 article-title: AI-facilitated health care requires education of clinicians publication-title: Lancet doi: 10.1016/S0140-6736(21)00722-4 – volume: 4 start-page: 93 year: 2021 ident: R13 article-title: Considering the possibilities and pitfalls of generative Pre-trained transformer 3 (GPT-3) in healthcare delivery publication-title: NPJ Digit Med doi: 10.1038/s41746-021-00464-x – volume: 5 year: 2020 ident: R3 article-title: Can GPT-3 pass a writer’s Turing test? publication-title: J Cult Anal doi: 10.22148/001c.17212 – year: 2020 ident: R6 article-title: Language Models are Few-Shot Learners [Internet] publication-title: arXiv [cs.CL] – volume: 19 start-page: 225 year: 2012 ident: R21 article-title: Importance of multi-modal approaches to effectively identify cataract cases from electronic health records publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2011-000456 – year: 2014 ident: R8 article-title: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation [Internet] publication-title: arXiv – volume: 1 start-page: e232 year: 2019 ident: R31 article-title: Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study publication-title: Lancet Digit Health doi: 10.1016/S2589-7500(19)30108-6 – volume: 469 start-page: 1859 year: 2011 ident: R17 article-title: Defining racial and ethnic disparities in pain management publication-title: Clin Orthop Relat Res doi: 10.1007/s11999-011-1770-9 – volume: 30 start-page: 681 year: 2020 ident: R5 article-title: GPT-3: its nature, scope, limits, and consequences publication-title: Minds Mach doi: 10.1007/s11023-020-09548-1 – volume: 47 start-page: 7 year: 2019 ident: R23 article-title: Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study publication-title: Clin Exp Ophthalmol doi: 10.1111/ceo.13340 – volume: 50 start-page: 1531 year: 2010 ident: R18 article-title: Individual differences in the subjective experience of pain: new insights into mechanisms and models publication-title: Headache doi: 10.1111/j.1526-4610.2010.01763.x – volume: 35 year: 2017 ident: R15 article-title: Cognitive technology addressing optimal cancer clinical trial matching and protocol feasibility in a community cancer practice publication-title: J Clin Orthod doi: 10.1200/JCO.2017.35.15_suppl.6501 – volume: 128 start-page: 942 year: 2021 ident: R29 article-title: Effectiveness of an ophthalmic hospital-based virtual service during the COVID-19 pandemic publication-title: Ophthalmology doi: 10.1016/j.ophtha.2020.10.012 – volume: 26 start-page: 328 year: 2016 ident: R27 article-title: Predictive modeling of risk factors and complications of cataract surgery publication-title: Eur J Ophthalmol doi: 10.5301/ejo.5000706 – volume: 17 start-page: 1277 year: 2008 ident: R26 article-title: Lower visual acuity predicts worse utility values among patients with type 2 diabetes publication-title: Qual Life Res doi: 10.1007/s11136-008-9399-1 – year: 2021 ident: R34 article-title: Learning transferable visual models from natural language supervision publication-title: arXiv – volume: 33 start-page: 34 year: 2020 ident: R30 article-title: Ambient virtual scribes: Mutuo health's AutoScribe as a case study of artificial intelligence-based technology publication-title: Healthc Manage Forum doi: 10.1177/0840470419872775 – volume: 74 start-page: 1026 year: 1986 ident: R1 article-title: Evaluation of natural language processing systems: issues and approaches publication-title: Proc IEEE Inst Electr Electron Eng doi: 10.1109/PROC.1986.13580 – ident: 2023062822101722000_106.7.889.2 doi: 10.1093/oso/9780198250791.003.0017 – volume: 4 start-page: 93 year: 2021 ident: 2023062822101722000_106.7.889.13 article-title: Considering the possibilities and pitfalls of generative Pre-trained transformer 3 (GPT-3) in healthcare delivery publication-title: NPJ Digit Med doi: 10.1038/s41746-021-00464-x – volume: 33 start-page: 34 year: 2020 ident: 2023062822101722000_106.7.889.30 article-title: Ambient virtual scribes: Mutuo health's AutoScribe as a case study of artificial intelligence-based technology publication-title: Healthc Manage Forum doi: 10.1177/0840470419872775 – ident: 2023062822101722000_106.7.889.9 – ident: 2023062822101722000_106.7.889.18 doi: 10.1111/j.1526-4610.2010.01763.x – ident: 2023062822101722000_106.7.889.11 – ident: 2023062822101722000_106.7.889.4 – ident: 2023062822101722000_106.7.889.6 – ident: 2023062822101722000_106.7.889.26 doi: 10.1007/s11136-008-9399-1 – ident: 2023062822101722000_106.7.889.33 – ident: 2023062822101722000_106.7.889.35 – volume: 47 start-page: 7 year: 2019 ident: 2023062822101722000_106.7.889.23 article-title: Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study publication-title: Clin Exp Ophthalmol doi: 10.1111/ceo.13340 – volume: 1 start-page: e232 year: 2019 ident: 2023062822101722000_106.7.889.31 article-title: Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study publication-title: Lancet Digit Health doi: 10.1016/S2589-7500(19)30108-6 – volume: 26 start-page: 378 year: 2017 ident: 2023062822101722000_106.7.889.28 article-title: Natural language processing to ascertain two key variables from operative reports in ophthalmology publication-title: Pharmacoepidemiol Drug Saf doi: 10.1002/pds.4149 – ident: 2023062822101722000_106.7.889.12 doi: 10.1126/science.aal4230 – volume: 120 year: 2021 ident: 2023062822101722000_106.7.889.14 article-title: Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases publication-title: J Biomed Inform doi: 10.1016/j.jbi.2021.103864 – volume: 397 year: 2021 ident: 2023062822101722000_106.7.889.32 article-title: AI-facilitated health care requires education of clinicians publication-title: Lancet doi: 10.1016/S0140-6736(21)00722-4 – ident: 2023062822101722000_106.7.889.21 doi: 10.1136/amiajnl-2011-000456 – ident: 2023062822101722000_106.7.889.7 doi: 10.1109/MICC.2017.8311752 – ident: 2023062822101722000_106.7.889.16 – volume: 74 start-page: 1026 year: 1986 ident: 2023062822101722000_106.7.889.1 article-title: Evaluation of natural language processing systems: issues and approaches publication-title: Proc IEEE Inst Electr Electron Eng doi: 10.1109/PROC.1986.13580 – ident: 2023062822101722000_106.7.889.8 doi: 10.3115/v1/D14-1179 – ident: 2023062822101722000_106.7.889.10 – ident: 2023062822101722000_106.7.889.15 doi: 10.1200/JCO.2017.35.15_suppl.6501 – ident: 2023062822101722000_106.7.889.19 doi: 10.1111/j.1525-1497.2006.00415.x – volume: 32 start-page: 397 year: 2021 ident: 2023062822101722000_106.7.889.20 article-title: Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions publication-title: Curr Opin Ophthalmol doi: 10.1097/ICU.0000000000000789 – ident: 2023062822101722000_106.7.889.17 doi: 10.1007/s11999-011-1770-9 – ident: 2023062822101722000_106.7.889.34 – ident: 2023062822101722000_106.7.889.36 – volume: 48 start-page: 169 year: 2020 ident: 2023062822101722000_106.7.889.25 article-title: Triaging ophthalmology outpatient referrals with machine learning: a pilot study publication-title: Clin Exp Ophthalmol doi: 10.1111/ceo.13666 – volume: 26 start-page: 328 year: 2016 ident: 2023062822101722000_106.7.889.27 article-title: Predictive modeling of risk factors and complications of cataract surgery publication-title: Eur J Ophthalmol doi: 10.5301/ejo.5000706 – ident: 2023062822101722000_106.7.889.29 doi: 10.1016/j.ophtha.2020.10.012 – volume: 137 start-page: 491 year: 2019 ident: 2023062822101722000_106.7.889.24 article-title: Evaluation of an algorithm for identifying ocular conditions in electronic health record data publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2018.7051 – ident: 2023062822101722000_106.7.889.5 doi: 10.1007/s11023-020-09548-1 – start-page: 51 year: 2000 ident: 2023062822101722000_106.7.889.22 article-title: Limited parsing of notational text visit notes: ad-hoc vs. NLP approaches – ident: 2023062822101722000_106.7.889.3 doi: 10.22148/001c.17212
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Snippet	Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently...
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Title	New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology
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