FUTURE OF THE LANGUAGE MODELS IN HEALTHCARE: THE ROLE OF CHATGPT

Abstract

ABSTRACT The field of medicine has always been at the forefront of technological innovation, constantly seeking new strategies to diagnose, treat, and prevent diseases. Guidelines for clinical practice to orientate medical teams regarding diagnosis, treatment, and prevention measures have increased over the years. The purpose is to gather the most medical knowledge to construct an orientation for practice. Evidence-based guidelines follow several main characteristics of a systematic review, including systematic and unbiased search, selection, and extraction of the source of evidence. In recent years, the rapid advancement of artificial intelligence has provided clinicians and patients with access to personalized, data-driven insights, support and new opportunities for healthcare professionals to improve patient outcomes, increase efficiency, and reduce costs. One of the most exciting developments in Artificial Intelligence has been the emergence of chatbots. A chatbot is a computer program used to simulate conversations with human users. Recently, OpenAI, a research organization focused on machine learning, developed ChatGPT, a large language model that generates human-like text. ChatGPT uses a type of AI known as a deep learning model. ChatGPT can quickly search and select pieces of evidence through numerous databases to provide answers to complex questions, reducing the time and effort required to research a particular topic manually. Consequently, language models can accelerate the creation of clinical practice guidelines. While there is no doubt that ChatGPT has the potential to revolutionize the way healthcare is delivered, it is essential to note that it should not be used as a substitute for human healthcare professionals. Instead, ChatGPT should be considered a tool that can be used to augment and support the work of healthcare professionals, helping them to provide better care to their patients.

Keywords

• Guidelines as topic
• Artificial intelligence
• Diagnosis
• Costs and cost analysis
• Delivery of health care