Large language model (LLM) and generative pre-trained transformers (GPT) in otolaryngology: Perspectives and limitations

The diagnosis and treatment of disorders affecting the ear, nose, throat,and adjacent structures is known as otolaryngology. With the advent of large language models (LLMs) and generative pre-trained transformers (GPTs), this area has made tremendous strides. Modern natural language processing (NLP) tools have the potential to transform otolaryngology practice in a number of ways, including patient education, clinical documentation, and research. Exceptionally good at comprehending and producing language that sounds human, LLMs and GPTs can efficiently and accurately transcribing clinical notes, simplifying the documentation process, and relieving otolaryngologists of administrative obligations. These models can also help extract pertinent data from medical records, which can lead to better decision-making and individualized treatment strategies. When it comes to patient education, LLMs and GPTs can provide customized instructions and explanations that improve understanding and treatment compliance. In the field of otolaryngology, where problems frequently entail extensive treatment procedures and complex anatomical components, this can be especially helpful. Additionally, by summarizing pertinent material, pointing out possible research gaps, and even helping to generate study proposals and hypotheses, these language models can expedite otolaryngology research. This can hasten the development of novel therapies, cuttingedge procedures, and diagnostic techniques. The integration of GPTs and LLMs in otolaryngology is not without difficulties, though. It is important to carefully consider issues including model bias, privacy concerns, and the possibility of producing false or misleading information. Ensuring the responsible and reliable implementation of these technologies in clinical settings will need the establishment of regulatory frameworks and ethical guidelines. This chapter examines the advantages and disadvantages of LLMs and GPTs in otolaryngology, going into detail about their underlying training protocols, architectures, and practical uses. We look at case stories and research results that demonstrate these models' transformative power while also talking about possible hazards and ethical issues. In order to provide comprehensive and intelligent solutions for otolaryngological care, we will offer insights into future approaches. One such direction is the integration of LLMs and GPTs with other new technologies, such computer vision and knowledge graphs.