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Can large language models replace standardised patients?

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Title: Can large language models replace standardised patients?
Authors: Han, Weipeng, Lyu, Xiaohong, Yang, Ji‐Jiang, Yan, Mengsha, Zhang, Yuelun, Wang, Tingyan, Pan, Hui, Chen, Shi, Zhu, Jiming, Huang, Xiaoming
Source: Medical Education. May2025, Vol. 59 Issue 5, p552-553. 2p.
Subjects: Medical education, Artificial intelligence, Natural language processing, Experience, Students, Simulated patients, Video recording
Abstract: The article discusses a study which evaluated the viability and effectiveness of large language models (LLM) as substitutes for standardised patients in medical education. The study tested open-source and closed-source LLMs and assessed the experiences of medical students. Lessons learned include less effectiveness of SPs than LLMs in students' psychological experiences, ability of LLMs to conduct simulated consultations, and higher examination difficulty and role-play assessment of SPs.
Database: Psychology and Behavioral Sciences Collection
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Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 184320819
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Can large language models replace standardised patients?
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  Data: <searchLink fieldCode="AR" term="%22Han%2C+Weipeng%22">Han, Weipeng</searchLink><br /><searchLink fieldCode="AR" term="%22Lyu%2C+Xiaohong%22">Lyu, Xiaohong</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Ji‐Jiang%22">Yang, Ji‐Jiang</searchLink><br /><searchLink fieldCode="AR" term="%22Yan%2C+Mengsha%22">Yan, Mengsha</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yuelun%22">Zhang, Yuelun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Tingyan%22">Wang, Tingyan</searchLink><br /><searchLink fieldCode="AR" term="%22Pan%2C+Hui%22">Pan, Hui</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Shi%22">Chen, Shi</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Jiming%22">Zhu, Jiming</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Xiaoming%22">Huang, Xiaoming</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Medical+Education%22">Medical Education</searchLink>. May2025, Vol. 59 Issue 5, p552-553. 2p.
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  Data: <searchLink fieldCode="DE" term="%22Medical+education%22">Medical education</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Experience%22">Experience</searchLink><br /><searchLink fieldCode="DE" term="%22Students%22">Students</searchLink><br /><searchLink fieldCode="DE" term="%22Simulated+patients%22">Simulated patients</searchLink><br /><searchLink fieldCode="DE" term="%22Video+recording%22">Video recording</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The article discusses a study which evaluated the viability and effectiveness of large language models (LLM) as substitutes for standardised patients in medical education. The study tested open-source and closed-source LLMs and assessed the experiences of medical students. Lessons learned include less effectiveness of SPs than LLMs in students' psychological experiences, ability of LLMs to conduct simulated consultations, and higher examination difficulty and role-play assessment of SPs.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=184320819
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        Value: 10.1111/medu.15641
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Artificial intelligence
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      – SubjectFull: Natural language processing
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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