Introduction: Artificial Intelligence (AI), including Large Language Models (LLM), is becoming increasingly significant in the field of medicine. Chat GPT, the most popular tool based on LLM technology, utilizes deep learning techniques. Currently, there is a growing body of research evaluating the potential applications of Chat GPT in medicine, and our study is based on an analysis of review articles from the largest medical database, PubMed. Aim of the study: The primary aim of this study was to conduct a comprehensive analysis of the utilization of Chat GPT in medicine, focusing on identifying its positive and negative aspects as well as limitations. State of Knowledge: Previous studies have demonstrated a rapid improvement in the efficacy of responses provided by Chat GPT. It has the potential to support diagnostics, therapy, patient communication, and medical education, thereby enhancing the quality of healthcare. However, there are certain issues associated with the implementation of this technology in the field of medical sciences, such as concerns regarding data sources, responsibility, and ethics when relying on responses generated by bots. Moreover, evident and concerning algorithmic flaws need to be addressed. Conclusions: Chat GPT, due to its accessibility and continuous improvement, has become one of the greatest hopes for the development of various industries, including medicine. It can be widely used to support educational processes and enhance efficiency in medicine. However, the use of such tools entails numerous challenges and issues, as they still have several drawbacks and limitations, such as ethical considerations and source verification. Further research on the utility of this tool is therefore necessary.
CITATION STYLE
Edyko, K., Petryla, P., Ostafin, K., Minkner, M., Bieńkowski, B., Feja, K., … Marchewka, U. (2023). Utilizing Artificial Intelligence Tools Using the GPT Chatbot in Medicine - A Review of Flaws, Advantages, and Limitations. Journal of Education, Health and Sport, 46(1), 122–133. https://doi.org/10.12775/jehs.2023.46.01.008
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