Virtual Posters
Asynchronous Session
Navigating Care: Child Life Therapeutic Language vs. AI in Pediatric Settings View Digital Media
Poster Session Holly Kihm
Child Life Specialists are health care professionals specially trained to provide support for pediatric patients and their families through the use of therapeutic play, diagnostic education, and age-appropriate procedural and surgical preparation. They extensively study child and adolescent development, in particular how to effectively communicate and ensure language used with patients is age and developmentally appropriate, soft, and conveys the right messages at the right time. With the advent and rapid development of artificial intelligence (AI) and its ability to transform concepts and language, it is important for health care providers to identify and understand the subtle and not so subtle differences in how AI would “speak” with patients and when AI may be used as a supplemental means of communication. A variety of AI sources were identified. A standard scenario was entered along with instructions for the AI platform to develop a script, depending on “who” the script was written for, including a patient’s diagnosis. A comparison of the AI generated scripts were made, and a separate comparison of the AI scripts with child life specialist scripts were made. While AI, across several platforms, generated useful scripts, they all missed one important component, the human factor and the ability of child life specialists to consider external factors when communicating with their patients. It is important to remember that communication is an art form, and while AI is honing its language every minute, child life specialists should continue to rely on their own communication skills when working with their patients.
From a Reductionistic Biomedical Model to a Holistic Healthcare Model: A Paradigm Shift View Digital Media
Poster Session James A. Marcum
In the twenty-first century, medicine and the healthcare system in general are undergoing a paradigm shift in their clinical gaze towards the patient. The shift is from a biomedical gaze that reduces the patient simply to big datasets to a holistic gaze that includes not just the patient’s personal data and narrative but also empowers the patient to participate in the healing or therapeutic process. In this study, two elements are examined in detail that are involved in this shift. The first is artificial intelligence, which provides clinicians with the computing power to make precise clinical decisions on how best to treat the patient based on big datasets. The second element is emotional intelligence, which allows the clinician to gaze empathically on the patient in terms of how the illness disrupts the patient’s life world, especially in terms of the patient’s values and preferences. The benefit of this shift from a reductionist biomedical model to holistic healthcare model is that artificial and emotional intelligence complement one another to provide patients with quality healthcare.
Mapping Scientific Reports for ChatGPT in Medical Education using Bibliometric Analysis View Digital Media
Poster Session Suzuka Kato
The usage of ChatGPT has grown sharply worldwide. Higher education institutions need to address the issue of its use. Previous studies reviewed and increased research into ChatGPT for educational research. This study maps scientific reports on ChatGPT in medical education. Focusing on hot topics and trends in ChatGPT for medical education, this study analyzed articles and reviews published between 2018 and 2024 for bibliometric analysis. Furthermore, the study explored authors' countries and publications from multiple countries. This study analyzed 736 papers, excluding those without reviews or articles from Web of Science, in the bibliometric analysis. This analysis explored the number of annual scientific reports, total citations and co-citations. Publication numbers increased beyond 2020. Many publications for ChatGPT and medical education were from the United States, China, India, Germany and Australia. Publications with a higher total citation score focused on the implications of ChatGPT for medicine and examinations. This finding provide innovative insights and examine models and theories for using ChatGPT in medical education.
