우울증 환자의 자살 위험 평가의 훈련을 위한 생성형 인공지능 챗봇의 의학적 교육 활용 사례: 일개 한의과대학 학생을 중심으로
Utilization of Generative Artificial Intelligence Chatbot for Training in Suicide Risk Assessment of Depressed Patients: Focusing on Students at a College of Korean Medicine
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Objectives: Among OECD countries, South Korea has been having the highest suicide rate since 2018, with 24.1 deaths per 100,000 people reported in 2020. The objectie of this study was to examine the use of generative artificial intellicence (AI) chatbots to train third-year Korean medicine (KM) students in conducting suicide risk assessments for patients with depressive disorders to train students for their clinical practice skills. Methods: The Claude 3 Sonnet model was utilized for chatbot simulations. Students performed mock consultations using standardized suicide risk assessment tools including Ask Suicide-Screening Questions (ASQ) tool and ASQ Brief Suicide Safety Assessment. Experiences and attitudes were collected through an anonymous online survey. Responses were rated on a 1∼5 Likert scale. Results: Thirty-six students aged 22∼30 years participated in this study. Their scores for interest and appropriateness (4.66±0.57), usefulness (4.60±0.61), and overall experience (4.63±0.60) were high. Their evaluation of the usability of artificial intelligence chatbot was also high at 4.58±0.70 points. However, their trust in chatbot responses (Q12) was lower (3.86±0.99). Common issues related to dissatisfaction included conversation disruptions due to token limits and inadequate chatbot responses. Conclusions: This is the first study investigating generative AI chatbots for suicide risk assessment training in KM education. Students reported high satisfaction, although their trust in chatbot accuracy was moderate. Technical limitations affected their experience. These preliminary findings suggest that generative AI chatbots hold promise for clinical training, particularly for education in psychiatry. However, improvements in response accuracy and conversation continuity are needed.
Abstract
Objectives: Among OECD countries, South Korea has been having the highest suicide rate since 2018, with 24.1 deaths per 100,000 people reported in 2020. The objectie of this study was to examine the use of generative artificial intellicence (AI) chatbots to train third-year Korean medicine (KM) students in conducting suicide risk assessments for patients with depressive disorders to train students for their clinical practice skills. Methods: The Claude 3 Sonnet model was utilized for chatbot simulations. Students performed mock consultations using standardized suicide risk assessment tools including Ask Suicide-Screening Questions (ASQ) tool and ASQ Brief Suicide Safety Assessment. Experiences and attitudes were collected through an anonymous online survey. Responses were rated on a 1∼5 Likert scale. Results: Thirty-six students aged 22∼30 years participated in this study. Their scores for interest and appropriateness (4.66±0.57), usefulness (4.60±0.61), and overall experience (4.63±0.60) were high. Their evaluation of the usability of artificial intelligence chatbot was also high at 4.58±0.70 points. However, their trust in chatbot responses (Q12) was lower (3.86±0.99). Common issues related to dissatisfaction included conversation disruptions due to token limits and inadequate chatbot responses. Conclusions: This is the first study investigating generative AI chatbots for suicide risk assessment training in KM education. Students reported high satisfaction, although their trust in chatbot accuracy was moderate. Technical limitations affected their experience. These preliminary findings suggest that generative AI chatbots hold promise for clinical training, particularly for education in psychiatry. However, improvements in response accuracy and conversation continuity are needed.
참고문헌 (26)
- Hwang JY, "Analysis of risk factors affecting suicidal ideation in South Korea by life cycle stage", Osong Public Health Res Perspect, 12, pp.314~323 (2021)[DOI]
- Cai H, "Prevalence of Suicidality in Major Depressive Disorder : A Systematic Review and Meta-Analysis of Comparative Studies", Front Psychiatry, 12, pp.690130~ (2021)[DOI]
- De Leo D, "Contacts with health professionals before suicide : missed opportunities for prevention?", Compr Psychiatry, 54, pp.1117~1123 (2013)[DOI]
- Ng CW, "Depression in primary care : assessing suicide risk", Singapore Med J, 58, pp.72~77 (2017)[DOI]
- Park J, "The Provision and Utilization of Traditional Korean Medicine in South Korea: Implications on Integration of Traditional Medicine in a Developed Country", Healthcare(Basel), 9 (2021)[DOI]
- Kwon CY, "Gap between the Scientificization and Utilization of Korean Medicine for Depressive Disorder in South Korea with the Highest Suicide Rate among OECD Countries", J Clin Med, 11 (2022)[DOI]
- Kwon CY, "The potential role of Korean medicine doctors in Korea’s suicide prevention policy : The first web-based survey", Asian J Psychiatr, 96, pp.104040~ (2024)[DOI]
- Sun L, "Artificial intelligence for healthcare and medical education : a systematic review", Am J Transl Res, 15, pp.4820~4828 (2023)
- Shoja MM, "The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice", Cureus, 15, pp.e40883~ (2023)[DOI]
- Han Y, "Usability and Educational Effectiveness of AI-based Patient Chatbot for Clinical Skills Training in Korean Medicine", Korean Journal of Acupuncture, 41, pp.27~32 (2024)[DOI]
- Kim J, "Pilot Development of a ‘Clinical Performance Examination(CPX)Practicing Chatbot’ Utilizing Prompt Engineering", J Korean Med, 45, pp.200~212 (2024)[DOI]
- Kim HJ, "The Right of the Clinical Training for the Medical Students and Privacy of the Patients", Korean Journal of Medicine and Law, 21, pp.107~123 (2013)
- Park HK, "Medical Education Using Standardized Patients", Hanyang Medical Reviews, 32, pp.35~44 (2012)[DOI]
- Uppalapati VK, "A Comparative Analysis of AI Models in Complex Medical Decision-Making Scenarios : Evaluating ChatGPT, Claude AI, Bard, and Perplexity", Cureus, 16, pp.e52485~ (2024)[DOI]
- Christensen LeCloux M, "Provider opinions of the acceptability of Ask Suicide-Screening Questions(ASQ)Tool and the ASQ Brief Suicide Safety Assessment(BSSA)for universal suicide risk screening in community healthcare : Potential barriers and necessary elements for future implementation", J Behav Health Serv Res, 49, pp.346~363 (2022)[DOI]
- Korea Patient Safety Reporting & Learning System, "Korean version of Ask Suicide-Screening Questions Toolkits", ~ (2020)
- Hong J, "A Survey of Students’ Satisfaction on Participation Learning Using Role-play in Clerkship", J Korean Acad Pediatr Dent, 42, pp.1~9 (2015)[DOI]
- Min Y, "A Comparative Study on the Usability by the Platfrom of Artificial Intelligence Chatbot Service in Library", Journal of the Korean Biblia Society for Library and Information Science, 34, pp.183~203 (2023)[DOI]
- Piot MA, "Simulation Training in Psychiatry for Medical Education : A Review", Front Psychiatry, 12, pp.658967~ (2021)[DOI]
- Wuendrich MS, "Portrayal of psychiatric disorders : are simulated patients authentic?", Acad Psychiatry, 36, pp.501~502 (2012)[DOI]
- Siemerkus J, "Using standardized patients for undergraduate clinical skills training in an introductory course to psychiatry", BMC Med Educ, 23, pp.159~ (2023)[DOI]
- Novais F, "Communication skills in psychiatry for undergraduate students : A scoping review", Front Psychiatry, 13, pp.972703~ (2022)[DOI]
- Ilieva G, "Effects of Generative Chatbots in Higher Education", Information, 14, pp.492~ (2023)
- Sidhu M, "What Do Medical Students Think About Incorporating VR Into Psychiatry Education and Training?", BJPsych Open, 8(Suppl 1), pp.S35~ (2022)[DOI]
- Bahadur AG, "Virtual Reality Simulation for Suicide Risk Assessment Training : Prevalence of Adverse Effects", Acad Psychiatry, 48, pp.57~60 (2024)[DOI]