Objective To determine the capabilities of ChatGPT for rapidly generating, rewriting, and evaluating (via diagnostic and triage accuracy) sets of clinical vignettes. Design We explored the capabilities of ChatGPT for generating and rewriting vignettes. First, we gave it natural language prompts to generate 10 new sets of 10 vignettes, each set for a different common childhood illness. Next, we had it generate 10 sets of 10 vignettes given a set of symptoms from which to draw. We then had it rewrite 15 existing pediatric vignettes at different levels of health literacy. Fourth, we asked it to generate 10 vignettes written as a parent, and rewrite these vignettes as a physician, then at a grade 8 reading level, before rewriting them from the original parent's perspective. Finally, we evaluated ChatGPT for diagnosis and triage for 45 clinical vignettes previously used for evaluating symptom checkers. Setting and participants ChatGPT, a publicly available, free chatbot. Main outcome measures Our main outcomes for de novo vignette generation were whether ChatGPT followed vignette creation instructions consistently, correctly, and listed reasonable symptoms for the disease being described. For generating vignettes from pre-existing symptom sets, we examined whether the symptom sets were used without introducing extra symptoms. Our main outcome for rewriting existing standardized vignettes to match patient demographics, and rewriting vignettes between styles, was whether symptoms were dropped or added outside the original vignette. Finally, our main outcomes examining diagnostic and triage accuracy on 45 standardized patient vignettes were whether the correct diagnosis was listed first, and if the correct triage recommendation was made. Results ChatGPT was able to quickly produce varied contexts and symptom profiles when writing vignettes based on an illness name, but overused some core disease symptoms. It was able to use given symptom lists as the basis for vignettes consistently, adding one additional (though appropriate) symptom from outside the list for one disease. Pediatric vignettes rewritten at different levels of health literacy showed more complex symptoms being dropped when writing at low health literacy in 87.5% of cases. While writing at high health literacy, it added a diagnosis to 80% of vignettes (91.7% correctly diagnosed). Symptoms were retained in 90% of cases when rewriting vignettes between viewpoints. When presented with 45 vignettes, ChatGPT identified illnesses with 75.6% (95% CI, 62.6% to 88.5%) first-pass diagnostic accuracy and 57.8% (95% CI, 42.9% to 72.7%) triage accuracy. Its use does require monitoring and has caveats, which we discuss. Conclusions ChatGPT was capable, with caveats and appropriate review, of generating, rewriting, and evaluating clinical vignettes.

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ChatGPT for Clinical Vignette Generation, Revision, and Evaluation

Can ChatGPT be used in oral and maxillofacial surgery?

Today, chatbots are being employed across a wide range of businesses. The researchers will present the outcome of the literature exploration on attributes of chatbots that later become a reference to build a new chatbot for banking use. The goal is to use human natural language as an interaction medium to aid humans in doing any prescribed activity. It is important to think about how people engage with chatbots in order to create a viable and high-quality chatbot solution. The authors aim to help the chatbot community by determining what qualities a chatbot should have and how to build a chatbot for a bank. For the purpose of compiling useful chatbot characteristics, the researchers searched through a group of linked papers. The survey was done on a small group of chatbot end users in order to get a sense of what functionalities are most important to the end users. On the basis of a summary of the literature research and data from a survey, a set of chatbot functional features is proposed. This research intended to provide a quality feature criterion for creating a chatbot application. 

Features requirement elicitation process for designing a chatbot application

In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study.

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

Representation of language is the first and critical task for Natural Language Understanding (NLU) in a dialogue system. Pretraining, embedding model, and fine-tuning for intent classification and slot-filling are popular and well-performing approaches but are time consuming and inefficient for low-resource languages. Concretely, the out-of-vocabulary and transferring to different languages are two tough challenges for multilingual pretrained and cross-lingual transferring models. Furthermore, quality-proved parallel data are necessary for the current frameworks. Stepping over these challenges, different from the existing solutions, we propose a novel approach, the Hypergraph Transfer Encoding Network “HGTransEnNet. The proposed model leverages off-the-shelf high-quality pretrained word embedding models of resource-rich languages to learn the high-order semantic representation of low-resource languages in a transductive clustering manner of hypergraph modeling, which does not need parallel data. The experiments show that the representations learned by “HGTransEnNet” for low-resource language are more effective than the state-of-the-art language models, which are pretrained on a large-scale multilingual or monolingual corpus, in intent classification and slot-filling tasks on Indonesian and English datasets.

Learning High-Order Semantic Representation for Intent Classification and Slot Filling on Low-Resource Language via Hypergraph

In recent years, gradual improvements in communication and connectivity technologies have enabled new technical possibilities for the adoption of chatbots across diverse sectors such as customer services, trade, and marketing. The chatbot is a platform that uses natural language processing, a subset of artificial intelligence, to find the right answer to all users’ questions and solve their problems. Advanced chatbot architecture that is extensible, scalable, and supports different services for natural language understanding (NLU) and communication channels for interactions of users has been proposed. The paper describes overall chatbot architecture and provides corresponding metamodels as well as rules for mapping between the proposed and two commonly used NLU metamodels. The proposed architecture could be easily extended with new NLU services and communication channels. Finally, two implementations of the proposed chatbot architecture are briefly demonstrated in the case study of “ADA” and “COVID-19 Info Serbia”.

Rade Matic

Papers

Extensible Chatbot Architecture Using Metamodels of Natural Language Understanding