ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?

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Abstract

ChatGPT, GPT-4, and Bard are highly advanced natural language process–based computer programs (chatbots) that simulate and process human conversation in written or spoken form. Recently released by the company OpenAI, ChatGPT was trained on billions of unknown text elements (tokens) and rapidly gained wide attention for its ability to respond to questions in an articulate manner across a wide range of knowledge domains. These potentially disruptive large language model (LLM) technologies have a broad range of conceivable applications in medicine and medical microbiology. In this opinion article, I describe how chatbot technologies work and discuss the strengths and weaknesses of ChatGPT, GPT-4, and other LLMs for applications in the routine diagnostic laboratory, focusing on various use cases for the pre- to post-analytical process.

Abstract

Natural language processing–based computer software simulates and processes human conversation. These potentially disruptive large language model technologies have a broad range of conceivable applications in medical microbiology. Strengths and weaknesses for applications in routine diagnostics are discussed.

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Most cited references 39

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial

Kathleen Kara Fitzpatrick, Alison M Darcy, Molly Vierhile (2017)

Background Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT.

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Evolutionary-scale prediction of atomic-level protein structure with a language model

Zeming Lin, Halil Akin, Roshan Rao … (2023)

Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins. Machine learning methods for protein structure prediction have taken advantage of the evolutionary information present in multiple sequence alignments to derive accurate structural information, but predicting structure accurately from a single sequence is much more difficult. Lin et al . trained transformer protein language models with up to 15 billion parameters on experimental and high-quality predicted structures and found that information about atomic-level structure emerged in the model as it was scaled up. They created ESMFold, a sequence-to-structure predictor that is nearly as accurate as alignment-based methods and considerably faster. The increased speed permitted the generation of a database, the ESM Metagenomic Atlas, containing more than 600 million metagenomic proteins. —MAF A protein language model enables structure prediction and analysis of more than 600 million metagenomic proteins.

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Author and article information

Contributors

Adrian Egli:

ORCID: https://orcid.org/0000-0002-3564-8603

Journal

Journal ID (nlm-ta): Clin Infect Dis

Journal ID (iso-abbrev): Clin Infect Dis

Journal ID (publisher-id): cid

Title: Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America

Publisher: Oxford University Press (US )

ISSN (Print): 1058-4838

ISSN (Electronic): 1537-6591

Publication date Collection: 01 November 2023

Publication date (Electronic): 03 July 2023

Publication date PMC-release: 03 July 2023

Volume: 77

Issue: 9

Pages: 1322-1328

Affiliations

Institute of Medical Microbiology, University of Zurich , Zurich, Switzerland

Author notes

Correspondence: A. Egli, Institute of Medical Microbiology, University of Zurich, Gloriastrasse 28, 8006 Zurich, Switzerland ( aegli@ 123456imm.uzh.ch ).

Potential conflicts of interest. A. E. reports grants or contracts from the Bangerter Rhyner Foundation and the Swiss National Science Foundation; consulting fees from Pfizer and Illumina; and participation on a data and safety monitoring board or advisory board for Sefunda.

The author has submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Author information

Adrian Egli https://orcid.org/0000-0002-3564-8603

Article

Publisher ID: ciad407

DOI: 10.1093/cid/ciad407

PMC ID: 10640689

PubMed ID: 37399030

SO-VID: 2c098ce7-507b-40ca-8200-6442af80ffe1

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This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 08 March 2023

Date: 22 June 2023

Date: 04 August 2023

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ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?

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Artificial Intelligence in Medicine

Most cited references 39

Highly accurate protein structure prediction with AlphaFold

Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial

Evolutionary-scale prediction of atomic-level protein structure with a language model

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