Virtual Patient Simulations Using Social Robotics Combined With Large Language Models for Clinical Reasoning Training in Medical Education: Mixed Methods Study
Gorde:
| Argitaratua izan da: | Journal of Medical Internet Research vol. 27 (2025), p. e63312 |
|---|---|
| Egile nagusia: | |
| Beste egile batzuk: | , , , , , , , |
| Argitaratua: |
Gunther Eysenbach MD MPH, Associate Professor
|
| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3222368694 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1438-8871 | ||
| 024 | 7 | |a 10.2196/63312 |2 doi | |
| 035 | |a 3222368694 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Borg, Alexander | |
| 245 | 1 | |a Virtual Patient Simulations Using Social Robotics Combined With Large Language Models for Clinical Reasoning Training in Medical Education: Mixed Methods Study | |
| 260 | |b Gunther Eysenbach MD MPH, Associate Professor |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background:Virtual patients (VPs) are computer-based simulations of clinical scenarios used in health professions education to address various learning outcomes, including clinical reasoning (CR). CR is a crucial skill for health care practitioners, and its inadequacy can compromise patient safety. Recent advancements in large language models (LLMs) and social robots have introduced new possibilities for enhancing VP interactivity and realism. However, their application in VP simulations has been limited, and no studies have investigated the effectiveness of combining LLMs with social robots for CR training.Objective:The aim of the study is to explore the potential added value of a social robotic VP platform combined with an LLM compared to a conventional computer-based VP modality for CR training of medical students.Methods:A Swedish explorative proof-of-concept study was conducted between May and July 2023, combining quantitative and qualitative methodology. In total, 15 medical students from Karolinska Institutet and an international exchange program completed a VP case in a social robotic platform and a computer-based semilinear platform. Students’ self-perceived VP experience focusing on CR training was assessed using a previously developed index, and paired 2-tailed t test was used to compare mean scores (scales from 1 to 5) between the platforms. Moreover, in-depth interviews were conducted with 8 medical students.Results:The social robotic platform was perceived as more authentic (mean 4.5, SD 0.7 vs mean 3.9, SD 0.5; odds ratio [OR] 2.9, 95% CI 0.0-1.0; P=.04) and provided a beneficial overall learning effect (mean 4.4, SD 0.6 versus mean 4.1, SD 0.6; OR 3.7, 95% CI 0.1-0.5; P=.01) compared with the computer-based platform. Qualitative analysis revealed 4 themes, wherein students experienced the social robot as superior to the computer-based platform in training CR, communication, and emotional skills. Limitations related to technical and user-related aspects were identified, and suggestions for improvements included enhanced facial expressions and VP cases simulating multiple personalities.Conclusions:A social robotic platform enhanced by an LLM may provide an authentic and engaging learning experience for medical students in the context of VP simulations for training CR. Beyond its limitations, several aspects of potential improvement were identified for the social robotic platform, lending promise for this technology as a means toward the attainment of learning outcomes within medical education curricula. | |
| 653 | |a Medical education | ||
| 653 | |a Foreign students | ||
| 653 | |a Rheumatic diseases | ||
| 653 | |a Clinical training | ||
| 653 | |a Exchange programs | ||
| 653 | |a Interactive computer systems | ||
| 653 | |a Questionnaires | ||
| 653 | |a Internal medicine | ||
| 653 | |a Robots | ||
| 653 | |a Simulated clients | ||
| 653 | |a Medical students | ||
| 653 | |a Authenticity | ||
| 653 | |a Internet | ||
| 653 | |a Medical technology | ||
| 653 | |a Facial expressions | ||
| 653 | |a Emotional intelligence | ||
| 653 | |a Robotics | ||
| 653 | |a Rheumatology | ||
| 653 | |a Patients | ||
| 653 | |a Qualitative research | ||
| 653 | |a Simulation | ||
| 653 | |a Clinical outcomes | ||
| 653 | |a Health care | ||
| 653 | |a Learning | ||
| 653 | |a Educational objectives | ||
| 653 | |a Clinical decision making | ||
| 653 | |a Multimedia | ||
| 653 | |a Medical personnel | ||
| 653 | |a Curricula | ||
| 653 | |a Large language models | ||
| 653 | |a Social education | ||
| 653 | |a Professions | ||
| 653 | |a Models | ||
| 653 | |a Self concept | ||
| 653 | |a Medical schools | ||
| 653 | |a Predicate | ||
| 653 | |a Computers | ||
| 653 | |a Computer mediated communication | ||
| 653 | |a Health education | ||
| 653 | |a Students | ||
| 653 | |a Training | ||
| 653 | |a Professional education | ||
| 653 | |a Health services | ||
| 653 | |a Reasoning | ||
| 653 | |a Attainment | ||
| 653 | |a Learning outcomes | ||
| 653 | |a Scores | ||
| 653 | |a Language modeling | ||
| 700 | 1 | |a Georg, Carina | |
| 700 | 1 | |a Jobs, Benjamin | |
| 700 | 1 | |a Huss, Viking | |
| 700 | 1 | |a Waldenlind, Kristin | |
| 700 | 1 | |a Ruiz, Mini | |
| 700 | 1 | |a Edelbring, Samuel | |
| 700 | 1 | |a Skantze, Gabriel | |
| 700 | 1 | |a Parodis, Ioannis | |
| 773 | 0 | |t Journal of Medical Internet Research |g vol. 27 (2025), p. e63312 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3222368694/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3222368694/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3222368694/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |