Generative artificial intelligence in health professions: a bibliometric descriptive analysis - Alves Lopes, António (My University, 2024)
Introduction and Objectives: Generative artificial intelligence (AI) refers to a type of AI that has the ability to create text, images and other media using models. These models learn patterns and structures, from training data in order to generate outputs. Generative AI finds applications in fields such as business, education, and healthcare. Some examples of AI systems include ChatGPT developed by OpenAI, Bard by Google and Claude from Anthropic. In the healthcare sector, generative AI has applications from gathering information during interactions between healthcare professionals and patients for creating clinical records to enhancing diagnostic accuracy and clinical efficiency to support continuity of care. Over the last year, there has been growth in the use of generative AI in healthcare with potential impacts on education and research as well. However, due to the amount of literature in this field comprehending its scientific structure and development presents challenges. To overcome this impact, visualization techniques based on data can prove helpful, for understanding the specific domains. Material and Methods: This is a bibliometric, descriptive, and retrospective study. The author identified publications from the PubMed database from November 2022 till November 2023 related to the use of Generative Artificial Intelligence in Health Professions, using this search string (("chatbot"[All Fields] OR "GPT"[All Fields] OR "ChatGPT"[All Fields] OR "Bard"[All Fields] OR "Bing"[All Fields]) AND ("Artificial Intelligence"[MeSH Terms] OR "Large Language Models"[All Fields] OR "LLM"[All Fields]) AND ("Health Personnel"[MeSH Terms] OR "Health Occupations"[MeSH Terms])) AND (2022/11/30:2023/11/30[pdat]). From the titles and abstracts of these publications, was selected the main terms related to the field, extracted by VOSviewer software, to create a visualization of the most important trends referred to in the literature. Results: The researchers identified a total of 248 relevant references, including clinical trials and randomized controlled trials, as well as meta-analyses and systematic reviews. Upon examining the co-occurrence of MeSH terms and authors' terms associated with Generative AI and healthcare professionals, we found that the most common association of terms was related to the medical profession across various medical specialities. This was followed by terms related to allied health professions. Another relevant observation was the dominance of ChatGPT from OpenAI in comparison to other chatbots trained on different Large Language Models. Conclusions: Overall, as shown by published research, the interest in Generative AI has grown exponentially, influencing all aspects related to the use of this approach in the practice, education, and research of healthcare professions. The use of generative AI has the potential to enhance the knowledge, clinical skills, and decision-making abilities of healthcare professionals, and ultimately lead to better patient outcomes. However, it is important to ensure that these technologies are designed and implemented in an ethical and responsible manner, with appropriate consideration given to issues such as bias, privacy, and transparency.
Keywords: Generative artificial intelligence; Health professions; Bibliometrics