Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment

Xiuhui Cao

doi:10.47297/taposatWSP2633-456929.20250611

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Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment

6卷第11期

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Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment
Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment
Geely University of China, Chengdu, Sichuan, 641423, China
2025年6卷第11期页码：148-151
纸质出版：2025-11-30
DOI：10.47297/taposatWSP2633-456929.20250611
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Xiuhui Cao. (2025). Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment. 科技理论与实践（英文版）, 6(11), 148-151.

Cao Xiuhui.Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment[J].Theory and Practice of Science and Technology,2025,06(11):148-151.
Xiuhui Cao. (2025). Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment. 科技理论与实践（英文版）, 6(11), 148-151. DOI： 10.47297/taposatWSP2633-456929.20250611.

Cao Xiuhui.Research Progress on a Generative AI-Powered Clinical Reasoning Training Model for Rehabilitation Assessment[J].Theory and Practice of Science and Technology,2025,06(11):148-151. DOI： 10.47297/taposatWSP2633-456929.20250611.

摘要

Abstract

Clinical reasoning competence is the core professional capability of rehabilitation therapists. Traditional training models are plagued by issues such as inadequate standardization

scalability challenges

and delayed feedback—problems that are particularly acute in private universities due to shortages of dual-qualified faculty and limited practical training resources. This paper proposes a "one-core

three-pillars" clinical reasoning training model for rehabilitation assessment based on generative AI

leveraging the open-source DeepSeek-R1 model as its technical foundation. Integrated into the practical teaching workflow of rehabilitation therapy programs

the model comprises three core functional modules: contradictory case generation

virtual supervisor inquiry

and competency profile diagnosis. A "three-dimension

six-index" evaluation system was constructed

and a longitudinal follow-up study was conducted to validate the model's effectiveness. The research findings aim to provide a low-cost

replicable AI-enabled solution for rehabilitation education in private universities

enrich the theoretical and practical framework of clinical reasoning training

and offer technical support for the standardized training of rehabilitation professionals in primary healthcare institutions.

关键词

Keywords

references

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