AI-driven platform for advancing clinical reasoning in musculoskeletal subjects

Fadi Mohammad Qassim Al Zoubi; Kangzhong Wang; Chin Kiu Jacky Fong; Eugene Fu; Jingxia Lin; Wai Hang Kwong; Chi Hang Kwok; Man Ha Tsang; Wai Lung Wong; Yu Lok Wong

AI-driven platform for advancing clinical reasoning in musculoskeletal subjects

Fadi Mohammad Qassim Al Zoubi
, Kangzhong Wang
, Chin Kiu Jacky Fong
, Eugene Fu
, Jingxia Lin
, Wai Hang Kwong
, Chi Hang Kwok
, Man Ha Tsang
, Wai Lung Wong
, Yu Lok Wong

Research output: Unpublished conference presentation (presented paper, abstract, poster) › Conference presentation (not published in journal/proceeding/book) › Academic research › peer-review

Abstract

We developed an innovative digital platform featuring interactive case studies for musculoskeletal conditions.
This platform was co-designed by researchers and practicing physiotherapists to ensure both clinical relevance
and educational quality. The platform integrates an AI-powered chatbot to simulate patient interactions
and guide physiotherapy students through realistic clinical reasoning scenarios. Targeted at entry-level
physiotherapy students, the platform was introduced during scheduled tutorial sessions as part of their formal
curriculum. The primary outcomes assessed were improvements in students' knowledge and clinical reasoning
skills. Additionally, qualitative feedback was gathered to capture students' perceptions of the platform's usability,
engagement, and educational value. Preliminary results indicate enhanced student understanding and greater
confidence in clinical decision-making, with positive feedback highlighting the platform’s interactivity and
real-world applicability. Our findings suggest that AI-driven, co-designed educational tools can effectively
supplement traditional physiotherapy education and foster critical clinical competencies in students.

Original language	English
Publication status	Not published / presented only - Dec 2025
Event	The 2nd International Conference on Teaching and Learning Innovations in Rehabilitation Sciences - Hong Kong, Hong Kong, Hong Kong Duration: 11 Dec 2025 → 12 Dec 2025

Competition

Competition	The 2nd International Conference on Teaching and Learning Innovations in Rehabilitation Sciences
Country/Territory	Hong Kong
City	Hong Kong
Period	11/12/25 → 12/12/25

Cite this

@conference{70b0ba70abd843bbabb28616f544fe6b,

title = "AI-driven platform for advancing clinical reasoning in musculoskeletal subjects",

abstract = "We developed an innovative digital platform featuring interactive case studies for musculoskeletal conditions. This platform was co-designed by researchers and practicing physiotherapists to ensure both clinical relevance and educational quality. The platform integrates an AI-powered chatbot to simulate patient interactions and guide physiotherapy students through realistic clinical reasoning scenarios. Targeted at entry-level physiotherapy students, the platform was introduced during scheduled tutorial sessions as part of their formal curriculum. The primary outcomes assessed were improvements in students' knowledge and clinical reasoning skills. Additionally, qualitative feedback was gathered to capture students' perceptions of the platform's usability, engagement, and educational value. Preliminary results indicate enhanced student understanding and greater confidence in clinical decision-making, with positive feedback highlighting the platform{\textquoteright}s interactivity and real-world applicability. Our findings suggest that AI-driven, co-designed educational tools can effectively supplement traditional physiotherapy education and foster critical clinical competencies in students.",

author = "\{Al Zoubi\}, \{Fadi Mohammad Qassim\} and Kangzhong Wang and Fong, \{Chin Kiu Jacky\} and Eugene Fu and Jingxia Lin and Kwong, \{Wai Hang\} and Kwok, \{Chi Hang\} and Tsang, \{Man Ha\} and Wong, \{Wai Lung\} and Wong, \{Yu Lok\}",

year = "2025",

month = dec,

language = "English",

note = "The 2nd International Conference on Teaching and Learning Innovations in Rehabilitation Sciences ; Conference date: 11-12-2025 Through 12-12-2025",

}

AI-driven platform for advancing clinical reasoning in musculoskeletal subjects. / Al Zoubi, Fadi Mohammad Qassim; Wang, Kangzhong; Fong, Chin Kiu Jacky et al.
2025. Paper presented at The 2nd International Conference on Teaching and Learning Innovations in Rehabilitation Sciences, Hong Kong, Hong Kong.

Research output: Unpublished conference presentation (presented paper, abstract, poster) › Conference presentation (not published in journal/proceeding/book) › Academic research › peer-review

TY - CONF

T1 - AI-driven platform for advancing clinical reasoning in musculoskeletal subjects

AU - Al Zoubi, Fadi Mohammad Qassim

AU - Wang, Kangzhong

AU - Fong, Chin Kiu Jacky

AU - Fu, Eugene

AU - Lin, Jingxia

AU - Kwong, Wai Hang

AU - Kwok, Chi Hang

AU - Tsang, Man Ha

AU - Wong, Wai Lung

AU - Wong, Yu Lok

PY - 2025/12

Y1 - 2025/12

N2 - We developed an innovative digital platform featuring interactive case studies for musculoskeletal conditions. This platform was co-designed by researchers and practicing physiotherapists to ensure both clinical relevance and educational quality. The platform integrates an AI-powered chatbot to simulate patient interactions and guide physiotherapy students through realistic clinical reasoning scenarios. Targeted at entry-level physiotherapy students, the platform was introduced during scheduled tutorial sessions as part of their formal curriculum. The primary outcomes assessed were improvements in students' knowledge and clinical reasoning skills. Additionally, qualitative feedback was gathered to capture students' perceptions of the platform's usability, engagement, and educational value. Preliminary results indicate enhanced student understanding and greater confidence in clinical decision-making, with positive feedback highlighting the platform’s interactivity and real-world applicability. Our findings suggest that AI-driven, co-designed educational tools can effectively supplement traditional physiotherapy education and foster critical clinical competencies in students.

AB - We developed an innovative digital platform featuring interactive case studies for musculoskeletal conditions. This platform was co-designed by researchers and practicing physiotherapists to ensure both clinical relevance and educational quality. The platform integrates an AI-powered chatbot to simulate patient interactions and guide physiotherapy students through realistic clinical reasoning scenarios. Targeted at entry-level physiotherapy students, the platform was introduced during scheduled tutorial sessions as part of their formal curriculum. The primary outcomes assessed were improvements in students' knowledge and clinical reasoning skills. Additionally, qualitative feedback was gathered to capture students' perceptions of the platform's usability, engagement, and educational value. Preliminary results indicate enhanced student understanding and greater confidence in clinical decision-making, with positive feedback highlighting the platform’s interactivity and real-world applicability. Our findings suggest that AI-driven, co-designed educational tools can effectively supplement traditional physiotherapy education and foster critical clinical competencies in students.

M3 - Conference presentation (not published in journal/proceeding/book)

T2 - The 2nd International Conference on Teaching and Learning Innovations in Rehabilitation Sciences

Y2 - 11 December 2025 through 12 December 2025

ER -

AI-driven platform for advancing clinical reasoning in musculoskeletal subjects

Abstract

Competition

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