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Skip search results from other journals and go to results- 4 JMIR Medical Informatics
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Transformers, introduced by Vaswani et al [20] in 2017, provided a novel approach to handling sequential data using self-attention mechanisms, thereby obviating the need for recurrent layers and significantly augmenting training efficiency. This pivotal innovation paved the way for the advent of progressively sophisticated and expansive models.
JMIR AI 2025;4:e57828
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Transformer-Based Tool for Automated Fact-Checking of Online Health Information: Development Study
The process included the following three steps: (1) Classification of web page content into 3 thematic categories, semiology, epidemiology, and management by evaluating various transformer models, including bidirectional encoder representations from transformers (BERT), Sci BERT, and Bio BERT, as well as traditional models such as random forest (RF) and support vector machine (SVM), (2) automating the creation of Pub Med queries combining “Wellcome Bert Mesh” and “Key BERT” models, (3) automatic extraction of top
JMIR Infodemiology 2025;5:e56831
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Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review
In the context of medical text, transformers excel in interpreting and extracting medically relevant information by effectively handling context and meaning, even in complex and specialized language.
Transformers can be trained as language models (LMs) on raw text in a self-supervised manner, enabling them to develop a statistical understanding of the text they were trained on [7]. However, the benefits of this approach are only fully realized when fine-tuning a downstream task.
JMIR Med Inform 2024;12:e60164
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Architectures of the first type, which include Bidirectional Encoder Representations from Transformers [59] and its variants [60-63], use only the encoder stack of the Transformer and are typically used in a transfer learning scenario to create dense representations of text for a particular downstream natural language processing task.
JMIR Res Protoc 2024;13:e60361
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Differences in the number of documents, sentences, and tokens between patients with atopic dermatitis (AD) and those without AD.
a BERT: Bidirectional Encoder Representations from Transformers.
Mean number of tokens for sentences identified in each category.
a BERT: Bidirectional Encoder Representations from Transformers.
JMIR Form Res 2024;8:e52200
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It used intent classifiers and transformers to understand and generate utterances, including MI reflections. In a pilot trial of 34 smokers, participants reported that the chatbot had a strong competency in MI but only scored 3 out of 5 on user satisfaction, leaving room for improvement.
The goal of this study was to determine the impact of several versions of an MI-oriented chatbot, which uses generative reflections, on moving smokers toward the decision to quit smoking.
JMIR Ment Health 2023;10:e49132
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ALBERT: A Lite Bidirectional Encoder Representations from Transformers; BERT: Bidirectional Encoder Representations from Transformers; LM: language model; SVM: support vector machine; TF-IDF: term frequency–inverse document frequency; RSDD: Reddit Self-reported Depression Diagnosis.
The results of the preemptive depression identification experiment are presented in Tables 5-8. Each table shows a variation in the number of matched control users.
JMIR AI 2023;2:e41205
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Recent studies have shown the effectiveness of transformers in classification tasks for EHR free-text data such as ICD coding [24,25], phenotyping [26], and readmission prediction [27].
JMIR AI 2023;2:e40843
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We designed a sentiment analysis pipeline based on pretrained transformers’ architectures. The output of transformers was combined via Dempster-Shafer theory (DST; theory of belief) to achieve higher accuracy in the recognition of sentiments. The performance of this model was verified for accuracy by using a manually annotated data set.
J Med Internet Res 2022;24(12):e41517
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Transformer-based neural networks like Bidirectional Encoder Representations from Transformers (BERT) [8,9] have achieved state-of-the-art results across a wide variety of natural language processing (NLP) tasks [10]. These models are pretrained on large amounts of text to learn general linguistic structure and produce contextualized representations of language.
JMIR Med Inform 2022;10(9):e38178
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