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      Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios

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          Abstract

          This paper aims to highlight the potential applications and limits of a large language model (LLM) in healthcare. ChatGPT is a recently developed LLM that was trained on a massive dataset of text for dialogue with users. Although AI-based language models like ChatGPT have demonstrated impressive capabilities, it is uncertain how well they will perform in real-world scenarios, particularly in fields such as medicine where high-level and complex thinking is necessary. Furthermore, while the use of ChatGPT in writing scientific articles and other scientific outputs may have potential benefits, important ethical concerns must also be addressed. Consequently, we investigated the feasibility of ChatGPT in clinical and research scenarios: (1) support of the clinical practice, (2) scientific production, (3) misuse in medicine and research, and (4) reasoning about public health topics. Results indicated that it is important to recognize and promote education on the appropriate use and potential pitfalls of AI-based LLMs in medicine.

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          Attention Is All You Need

          Ashish Vaswani, Noam Shazeer, Niki Parmar … (2017)
          The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures
          Article 0 comments Cited 2432 times – based on 0 reviews     Review now
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            Abstracts written by ChatGPT fool scientists

            Holly Else (2023)
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              GPT-3: Its Nature, Scope, Limits, and Consequences

              Luciano Floridi, Massimo Chiriatti (2020)
              In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic (that is, the Turing Test), and ethical questions and show that GPT-3 is not designed to pass any of them. This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts.
              Article 0 comments Cited 150 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Elena Bignami: elenagiovanna.bignami@unipr.it
                Journal
                Journal ID (nlm-ta): J Med Syst
                Journal ID (iso-abbrev): J Med Syst
                Title: Journal of Medical Systems
                Publisher: Springer US (New York )
                ISSN (Print): 0148-5598
                ISSN (Electronic): 1573-689X
                Publication date (Electronic): 4 March 2023
                Publication date PMC-release: 4 March 2023
                Publication date (Print): 2023
                Volume: 47
                Issue: 1
                Electronic Location Identifier: 33
                Affiliations
                [1 ]GRID grid.508451.d, ISNI 0000 0004 1760 8805, Department of Anesthesia and Critical Care, , Istituto Nazionale Tumori - IRCCS, Fondazione Pascale, ; Via Mariano Semmola, 53, 80131 Naples, Italy
                [2 ]GRID grid.414614.2, Department of Anesthesia and Intensive Care, , Infermi Hospital, AUSL Romagna, ; Viale Settembrini 2, 47923 Rimini, Italy
                [3 ]GRID grid.10383.39, ISNI 0000 0004 1758 0937, Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, , University of Parma, ; Viale Gramsci 14, 43126 Parma, Italy
                Article
                Publisher ID: 1925
                DOI: 10.1007/s10916-023-01925-4
                PMC ID: 9985086
                PubMed ID: 36869927
                SO-VID: c351b560-ab00-4c77-8896-a77c93dbd141
                Copyright © © The Author(s) 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 21 January 2023
                Date accepted : 20 February 2023
                Funding
                Funded by: Università degli Studi di Parma
                Open Access :

                Open access funding provided by Università degli Studi di Parma within the CRUI-CARE Agreement.

                Funded by: Università degli Studi di Parma
                Open Access :

                Open access funding provided by Università degli Studi di Parma within the CRUI-CARE Agreement.

                Categories
                Subject: Brief Report
                Custom metadata
                issue-copyright-statement © Springer Science+Business Media, LLC, part of Springer Nature 2023

                ScienceOpen disciplines: Public health
                Keywords: artificial intelligence,chatgpt,medicine,clinical resaerch
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: artificial intelligence, chatgpt, medicine, clinical resaerch

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