# Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
## 2 Background  
NLP is a pivotal subfield of artificial intelligence (AI) that focuses on the interaction between computers and human language. Its primary objective is to enable machines to comprehend, interpret, and generate human language in a meaningful and contextually relevant manner. NLP encompasses a wide spectrum of tasks, including language translation, sentiment analysis, speech recognition, text summarization, question answering, and more, each designed to bridge the gap between human communication and computational understanding [9]. NLP has witnessed extensive implementation in healthcare, showcasing its remarkable ability to extract and analyze valuable insights from vast amounts of unstructured clinical data, including EHRs, medical literature, and patient-generated content [10]. Notably, NLP has shown significant promise in converting unstructured clinical notes into structured data, helping to identify medical conditions, medications, and lab test names through tasks like named entity recognition [11; 12]. Similarly, NLP has been applied to unstructured EHR data to identify and detect adverse drug events using identified drugs and their interactions [13]. NLP-driven models have also contributed to early disease detection, enabling timely interventions and improving patient outcomes [14]. Within the realm of NLP, the GPT models have emerged as a noteworthy development pioneered by OpenAI. GPT models constitute a family of sophisticated language models designed to harness the potential of deep-learning neural networks. The transformer architecture, first proposed by [PERSON] et al. in 2017 [15], underlies the functioning of GPT models. This neural network architecture excels in processing sequential data, especially text, by employing \"self-attention\". Self-attention enables the model to understand the importance of and relationships between individual words in a sentence during processing, allowing it to give higher attention to semantically related words and carry more contextual information. The revolutionary aspect of transformers lies in their capability to handle long-range dependencies within language sequences efficiently, enhancing their proficiency in contextual language understanding and semantic comprehension. Since their reception, the development and evolution of GPT models have witnessed rapid progress over a relatively short period. The timeline of GPT models begins with the introduction of the original GPT-1 in June 2018 by OpenAI. GPT-1 demonstrated the potential of pre-training large-scale Transformer models on vast amounts of text data to generate coherent and contextually relevant language. Building upon the success of GPT-1, OpenAI released GPT-2 in February 2019. GPT-2 was a significant advancement, boasting an impressive 1.5 billion parameters, but its release raised concerns about potential misuse due to its ability to generate human-like text, including fake news and misinformation. As a result, OpenAI initially limited the release of GPT-2, only providing access to smaller versions of the model. In November 2019, OpenAI fully released the GPT-2 model, making it widely accessible to researchers and developers. This move led to an explosion of research and experimentation, driving the rapid development and fine-tuning of GPT-2 across various domains, including text generation, chatbots, language translation, and more [1; 16]. The timeline took another leap in June 2020 with the release of GPT-3. This model was a monumental achievement, consisting of a staggering 175 billion parameters, making it the largest language model ever created at that time. GPT-3 demonstrated unparalleled language generation capabilities, including the ability to perform tasks like translation, summarization, question-answering, and even creative writing. GPT-3's success ignited further interest in LLMs, leading to numerous research breakthroughs and commercial applications [1].