## 3 Methods
### Bibliometric Indicators
#### 3.2.1 Temporal Analysis: How Does It Change Over Time? Temporal analysis allows us to track the evolution of _LLM_s in healthcare, highlighting significant trends and changes. It helps identify how the adoption of _LLM_s has grown or changed over time, pinpointing key moments (e.g., after the introduction of ChatGPT). Moreover, it highlights how academic and clinical interest has increased or shifted, and which areas (diagnosis, training, etc.) have received more attention at different stages. It can demonstrate the impact of new versions of _LLM_s (such as GPT-3 or GPT-4) on their use in healthcare. In our study, we searched the publications starting from 1995, which can be considered as a watershed, and are divided into three parts, as shown below. _Period 1 (1995-2017): \"Pioneering Period\" or \"Pre-Transformer Era\"_. This period is characterized by by foundational advancements in neural networks and the use of simpler language models, such as rule-based models, Naive Bayes, Hidden Markov Models (HMM), and classical machine learning models. The use of language models was limited to specific tasks, such as information extraction from medical texts and managing electronic health records (EHRs). The proliferation of the internet and large-scale digital content provided vast amounts of text and structured data, essential for training language models. Public datasets like Wikipedia, news corpora, and large repositories of scientific literature became standard training resources. Moreover, the advancements in computational power due to the development and accessibility of Graphics Processing Units (GPUs), followed by specialized hardware like Tensor Processing Units (TPUs), enabled efficient parallel processing, drastically reducing the time and cost required to train deep neural networks. The introduction of Word2 Vec (2013) significantly improved the vector representations of words. The introduction of new activation functions (such as ReLU), regularization techniques (e.g., dropout), and advanced optimization algorithms (like Adam) improved model stability and performance. Breakthroughs in unsupervised pretraining (e.g., Word2 Vec in 2013 [30]) demonstrated the power of using vast text data to learn meaningful word representations. IBM Watson (2011) demonstrated the applicability of AI in the medical field, especially in diagnosis. The period witnessed critical steps in evolving neural network designs, with long short-term memory (LSTM) and gated recurrent units (GRU) providing solutions for modeling sequential data. These architectures addressed limitations in processing long-range dependencies in text but still faced challenges in scaling. In 2012, AlexNet's success in the ImageNet competition highlighted the potential of deep learning for image classification, influencing natural language processing (NLP) research. The same momentum carried into NLP with encoder-decoder models, culminating in the transformer architecture (introduced in 2017). Despite these advances, the pre-2017 period was marked by limitations in scaling models. However, it provided a fertile ground for the breakthroughs that followed, particularly the transformer, which replaced recurrence with attention mechanisms and laid the foundation for modern large language models. This period can, thus, be seen as an incubation phase for the _LLM_ revolution, driven by continuous progress in data, computing power, and innovative algorithms. _Period 2 (2018-2022): \"Period of Transformative Innovation\"._ This period marks the advent of Transformers, such as BERT by Google in 2018 and OpenAI's GPT-2 in 2019, which revolutionized natural language processing. It was a time of accelerated innovation and increasingly widespread adoption in the medical field, and impressive improvement in the ability to process natural language. Usage includes medical text classification, symptom-based diagnosis, and support for scientific research, as well as the implementation of chatbots for healthcare assistance and predictive analytics.