Use of Artificial Intelligence in Legal Technologies

Topal, M. Onat; Heerden, Imke van; Baş, Anıl

doi:10.31273/eirj.v8i3.788

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…In this context, the state of the art in NLP is represented by DL architectures such as GPT, XLNet, or BERT [61]. Among these, BERT has been found to be particularly widely used in the medical field in general, and for stroke in particular, along with specialized versions fitted to these applications that improve their performance [22,41].…”

Section: Discussionmentioning

confidence: 99%

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

De Rosario,

Pitarch-Corresa,

Pedrosa

et al. 2023

JMIR Med Inform

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Background Recent advances in natural language processing (NLP) have heightened the interest of the medical community in its application to health care in general, in particular to stroke, a medical emergency of great impact. In this rapidly evolving context, it is necessary to learn and understand the experience already accumulated by the medical and scientific community. Objective The aim of this scoping review was to explore the studies conducted in the last 10 years using NLP to assist the management of stroke emergencies so as to gain insight on the state of the art, its main contexts of application, and the software tools that are used. Methods Data were extracted from Scopus and Medline through PubMed, using the keywords “natural language processing” and “stroke.” Primary research questions were related to the phases, contexts, and types of textual data used in the studies. Secondary research questions were related to the numerical and statistical methods and the software used to process the data. The extracted data were structured in tables and their relative frequencies were calculated. The relationships between categories were analyzed through multiple correspondence analysis. Results Twenty-nine papers were included in the review, with the majority being cohort studies of ischemic stroke published in the last 2 years. The majority of papers focused on the use of NLP to assist in the diagnostic phase, followed by the outcome prognosis, using text data from diagnostic reports and in many cases annotations on medical images. The most frequent approach was based on general machine learning techniques applied to the results of relatively simple NLP methods with the support of ontologies and standard vocabularies. Although smaller in number, there has been an increasing body of studies using deep learning techniques on numerical and vectorized representations of the texts obtained with more sophisticated NLP tools. Conclusions Studies focused on NLP applied to stroke show specific trends that can be compared to the more general application of artificial intelligence to stroke. The purpose of using NLP is often to improve processes in a clinical context rather than to assist in the rehabilitation process. The state of the art in NLP is represented by deep learning architectures, among which Bidirectional Encoder Representations from Transformers has been found to be especially widely used in the medical field in general, and for stroke in particular, with an increasing focus on the processing of annotations on medical images.

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Section: Discussionmentioning

confidence: 99%

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

De Rosario,

Pitarch-Corresa,

Pedrosa

et al. 2023

JMIR Med Inform

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show abstract

“…Developed by OpenAI, GPT stands as a remarkable example of the transformative capabilities of large-scale neural language models [ 44 ]. At its core, GPT is founded upon the innovative Transformer architecture, a model that has revolutionized the field by effectively capturing long-term dependencies within sequences, making it exceptionally well-suited for tasks involving language understanding and generation [ 45 , 46 ]. The GPT family has multiple versions: The GPT-2 model, with 1.5 billion parameters, is capable of generating extensive sequences of text while adapting to the style and content of arbitrary inputs [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events

Li,

et al. 2024

PLoS ONE

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Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs’ capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs’ potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

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“…Different transformer-based models are used in different natural language processing (NLP) tasks, including text generation tasks such as novel/story writing, customer services. These models produce state of the art performances based on their parameters (Topal et al, 2021). The transformers have a better way of dealing with RNN issues because they use an attention mechanism to eliminate the sequential processing of a text that RNN performs by focusing on the current word (Bin et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

The Analysis of a GPT-based Sepedi Text Generation Model

Moila¹,

Modipa²,

Manamela³

2022

ICONIC

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Text generation is defined as a component of natural language processing that makes use of computational linguistics techniques to produce text that cannot be distinguished from human written text. This study aims to develop and analyse a Generative Pre-Trained Transformer 2 (GPT-2) language model to generate Sepedi phrases. The under-resourced Sepedi language is regarded as a disjunctive language. The Sepedi language orthographic representation presents challenges and has limited resources. The GPT-2 transformer requires large datasets, as well as state-of-the-art computational resources. The unstructured National Centre for Human Language Technology (NCHLT) Sepedi text dataset was used. The text generation model developed with the small dataset managed to get the lowest loss value of 2.36. The output text generated using this model produces a text that is syntactically correct with instances of grammatical errors. The model performed better than previously developed Sepedi text generation models by using transformer-based technique.

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Use of Artificial Intelligence in Legal Technologies

Cited by 4 publications

References 2 publications

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events

The Analysis of a GPT-based Sepedi Text Generation Model

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