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Generative AI-Based Evaluation of Clinical Thinking and Decision-Making in Medical Students

30 oktober 2026 uppdaterad av: Danzhi Chen, Sir Run Run Shaw Hospital

Construction of an Intelligent Evaluation System for Clinical Thinking and Decision-making Ability of Medical Students Based on Generative Artificial Intelligence

The goal of this study is to develop and evaluate an intelligent system based on generative artificial intelligence (AI) for assessing clinical thinking and decision-making abilities in medical students.

The study will examine whether the AI-based evaluation system can provide reliable and valid assessments of medical students' performance in clinical scenarios. Participants will complete clinical case-based tasks designed to assess their clinical reasoning and decision-making abilities. Their responses will be evaluated using the AI-based system and compared with assessments made using established evaluation methods and/or expert ratings.

The findings of this study may help determine whether generative AI can be used as a practical tool for assessing clinical thinking and decision-making skills in medical education.

Studieöversikt

Detaljerad beskrivning

This study aims to develop and evaluate a generative artificial intelligence-based system for assessing clinical thinking and decision-making abilities in medical students. Clinical reasoning and decision-making are core competencies in medical education, but conventional assessment methods often require substantial faculty time and may be influenced by differences among evaluators.

The study will develop an intelligent evaluation framework using generative artificial intelligence to analyze medical students' responses to clinical case-based tasks. The system will assess key aspects of clinical thinking and decision-making, such as information interpretation, diagnostic reasoning, selection of appropriate management strategies, and overall decision-making logic.

Medical students participating in the study will complete standardized clinical scenarios or case-based assessment tasks. Their performance will be evaluated using the artificial intelligence-based system and compared with evaluations made by qualified clinical experts and/or established assessment methods. The study will examine the reliability, validity, consistency, and feasibility of the artificial intelligence-based evaluation approach.

The purpose of this study is to determine whether generative artificial intelligence can serve as a reliable and practical tool for evaluating clinical thinking and decision-making abilities in medical education. The system is intended for educational assessment and is not designed to replace clinical diagnosis or treatment decisions.

Studietyp

Interventionell

Inskrivning (Faktisk)

90

Fas

  • Inte tillämpbar

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studieorter

    • Zhejiang
      • Hangzhou, Zhejiang, Kina, 310000
        • Sir Run Run Shaw Hospital, Zhejiang University School of Medicine

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

18 år till 30 år (Vuxen)

Tar emot friska volontärer

Nej

Kön som är behöriga för studier

Allt

Beskrivning

Inclusion Criteria:

  • Aged 18 to 30 years.
  • Fourth- or fifth-year undergraduate students majoring in Clinical Medicine at Zhejiang University School of Medicine, or trainees who have completed at least one year of standardized residency training at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine.
  • Have completed basic surgery courses and theoretical learning related to breast diseases.
  • Voluntarily agree to participate in the study and provide written informed consent.
  • Have adequate communication and comprehension abilities.

Exclusion Criteria:

  • Have previously participated in similar generative artificial intelligence (GAI)-based teaching intervention studies.
  • Are unable to complete the entire study because of rotation schedules or other conflicts.
  • Belong to vulnerable populations other than employees of the study institution or subordinates of the investigators, including individuals with mental illness, cognitive impairment, critical illness, pregnancy, or illiteracy.
  • Refuse or are unwilling to provide informed consent.

Studieplan

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

  • Primärt syfte: Övrig
  • Tilldelning: Randomiserad
  • Interventionsmodell: Parallellt uppdrag
  • Maskning: Enda

Vapen och interventioner

Deltagargrupp / Arm
Intervention / Behandling
Aktiv komparator: Control Group: Traditional Teaching

the standardized surgical oncology teaching curriculum, utilizing standardized breast cancer teaching PowerPoint courseware.

Participants will receive standard traditional teaching, including routine classroom instruction, case-based learning, and teacher-guided discussion, without the use of the generative artificial intelligence-assisted learning component.

Experimentell: Intervention Group: Traditional Teaching Plus AI Assistance

the independently developed "Clinical Reasoning and Decision-Making Dialogue Evaluation System" on the basis of the same traditional teaching.

Participants will receive standard traditional teaching, including routine classroom instruction, case-based learning, and teacher-guided discussion, without the use of the generative artificial intelligence-assisted learning component.

The intervention consists of traditional medical education supplemented with a generative artificial intelligence-based learning tool. The tool will support students in clinical case analysis, reasoning, and decision-making exercises. The control group will receive the same traditional teaching without access to the AI-assisted learning component.

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Change in Clinical Reasoning Score on Objective Structured Clinical Examination (OSCE)
Tidsram: Baseline to Week 4

The total score ranges from 0 to 100. A higher score indicates better clinical thinking and decision-making ability

Baseline to Week 4

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Intraclass Correlation Coefficient (ICC) Between GAI-Based and Expert Assessment Scores
Tidsram: Baseline to Week 4

Both Generative AI (GAI) and human experts will evaluate the participants using the clinical Reasoning Assessment Rubric. The agreement between the GAI-based scores and expert assessment scores will be evaluated using the Intraclass Correlation Coefficient (ICC) . The coefficient ranges from 0 to 1, where higher values indicate better agreement.

Baseline to Week 4
Time to Complete the Assessment
Tidsram: During the clinical thinking and decision-making assessment at Week 4 (completion time measured in minutes).

Assessment completion time is defined as the duration, measured in minutes, from the initiation of the clinical thinking and decision-making assessment to its final submission by the participant. The time will be automatically recorded by the evaluation platform. Each participant's assessment completion time will be measured during the assessment conducted at Week 4.

During the clinical thinking and decision-making assessment at Week 4 (completion time measured in minutes).
Participant Satisfaction Score on the GAI-Assisted Evaluation System
Tidsram: Week 4

Participant satisfaction will be measured using a 5-point Likert scale (ranging from 1 = Very Dissatisfied to 5 = Very Satisfied). A higher score indicates higher satisfaction with the GAI-assisted evaluation system.

Week 4

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Faktisk)

26 september 2025

Primärt slutförande (Faktisk)

25 december 2025

Avslutad studie (Faktisk)

25 december 2025

Studieregistreringsdatum

Först inskickad

9 oktober 2026

Först inskickad som uppfyllde QC-kriterierna

30 oktober 2026

Första postat (Faktisk)

4 november 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

4 november 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

30 oktober 2026

Senast verifierad

1 oktober 2026

Mer information

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