A study on the application of the remote multidisciplinary team teaching model in clinical postgraduate education of urology

Abstract

Background

Postgraduate medical education increasingly requires learners to develop clinical reasoning, manage uncertainty, integrate heterogeneous clinical information and collaborate across disciplines. Multidisciplinary team (MDT) teaching may provide a clinically situated learning environment in which trainees can observe and participate in complex decision-making. However, evidence on the use of remote MDT teaching in urology postgraduate education remains limited.

Methods

This randomized controlled study included 76 professional master’s students rotating through a surgical standardized residency training base. Students were assigned to a remote MDT teaching group or a conventional teaching group, with 38 students in each group. The remote MDT intervention was delivered as a structured teaching package that included pre-session case preparation, remote multidisciplinary case discussion, expert-guided case analysis, post-session reflection and iterative feedback. Outcomes included theoretical knowledge, clinical decision-making, practical skills, questionnaire-based evaluation of the teaching model and overall competency.

Results

Students in the remote MDT group achieved higher scores than those in the conventional teaching group in theoretical knowledge, clinical decision-making, questionnaire-based evaluation and overall competency. No significant between-group difference was observed in practical skills. The pattern of findings suggested that the remote MDT model had a stronger effect on outcomes requiring information integration, clinical reasoning and collaborative decision-making than on procedural performance.

Conclusions

Remote MDT teaching may be a useful component of urology postgraduate education, particularly for learning outcomes related to clinical reasoning and knowledge integration. Its value appears to derive not only from remote delivery, but also from the structured organization of preparation, multidisciplinary discussion, reflection and feedback. Procedural skill development remains dependent on supervised hands-on practice. Future multicentre studies are needed to confirm these findings and to examine which components of the intervention contribute most to learning.

Trial registration

Not applicable.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12909-026-09789-1.

Introduction

Medical education is undergoing substantial change in response to increasing clinical complexity, an expanding evidence base and the rapid integration of digital technologies into clinical practice. In contemporary healthcare, physicians are expected not only to master core medical knowledge, but also to manage clinical uncertainty, integrate heterogeneous clinical information and collaborate across disciplinary boundaries. For medical trainees, particularly at the postgraduate stage, this period represents a critical transition from knowledge acquisition to the development of independent clinical reasoning. The rapidly changing clinical environment also places increasing demands on learners’ cognitive load and adaptability [1, 2]. Traditional teaching methods, especially lecture-based instruction, remain centred on the transmission of information. Although such methods are efficient for delivering standardized knowledge, they are less able to support higher-order abilities such as integrative reasoning, clinical judgment and decision-making under uncertainty. With the development of evidence-based medicine and team-based care, the limitations of conventional teaching models have become increasingly apparent [3].

The multidisciplinary team (MDT) model, widely used in the management of complex diseases such as urological malignancies, provides a potential educational response to these challenges. In real MDT practice, learners encounter multiple professional perspectives and can observe how disagreements are negotiated, how clinical decisions are justified and how consensus is reached through evidence-informed discussion [4–7]. Remote MDT platforms further extend this process across institutions, allowing learners to access a broader range of expert input and clinical cases than is usually available in conventional single-centre teaching. This wider exposure may enrich clinical perspectives and support the development of reasoning that can be transferred across settings [8, 9]. At the same time, remote participation requires learners to prepare independently, engage actively in discussion and reflect systematically after the session. These features are consistent with the broader literature on interprofessional education, which emphasizes collaborative learning, communication and competency development in clinical training [10–12]. The learning process is also compatible with social constructivist, situated learning and cognitive apprenticeship perspectives, which emphasize interaction, participation in authentic practice and expert-guided reflection [13–15].

Beyond online delivery, digital platforms and Artificial intelligence (AI)-supported search functions can help faculty prepare remote MDT sessions, but their role needs to be limited and transparent. AI has been increasingly explored in urology for retrieving guideline-based information, generating patient-oriented explanations and supporting clinical education. Beyatli et al. reported that ChatGPT-4 could provide generally accurate responses to both guideline-derived and patient-focused questions in upper tract urothelial carcinoma, including ureteral cancer, suggesting its potential value in organizing disease-specific evidence and improving access to educational information [16]. In the present model, AI was used by faculty before teaching to collect recent guideline statements and related literature, organize case information from urology, imaging, pathology, oncology, radiotherapy, and surgical decision-making, and draft short prompts for discussion. The outputs were not given to trainees as stand-alone answers. Faculty checked the retrieved information against guidelines and primary literature, edited the wording, and then uploaded only the reviewed case packets, evidence notes, discussion questions, mind maps, and post-session review points to the teaching platform. Trainees therefore used AI-informed but faculty-checked materials to support questioning and evidence verification, not to make clinical decisions independently. This distinction is important because recent urological studies have shown both the usefulness and the limitations of large language models in urology [16, 17]. Katsimperis et al. suggested that AI has begun to be incorporated into urology training as a learning aid, but its accuracy in specialized urological knowledge remains limited and requires supervision by clinical teachers [17, 18]. Therefore, AI can serve as a tool to enhance learning efficiency, but it cannot replace teachers. This means that the final decision-making power still lies with the communication and confirmation between teachers and students.

Despite these theoretical and educational advantages, the educational value of remote MDT teaching in postgraduate medical training has not been sufficiently examined. Existing studies have not fully clarified how remote MDT teaching influences different dimensions of clinical competency, including knowledge acquisition, clinical decision-making, procedural skills and broader professional competence. Evidence is particularly limited in urology postgraduate education, where complex cases often require coordinated input from urology, oncology, radiotherapy, pathology and imaging. This randomized controlled study therefore evaluated the effectiveness of a remote MDT teaching model in urology postgraduate education. By examining multiple learning outcomes, the study aimed to clarify whether a technology- enhanced, cross-disciplinary learning environment can improve clinical reasoning, decision-making and overall competency among postgraduate trainees.

Methods

Participants

This study used a prospective randomized controlled design to evaluate the educational effectiveness of a remote multidisciplinary team (MDT) teaching model in urology postgraduate education. The study was conducted at a surgical standardized residency training base from January 2025 to December 2025. The study design and workflow of the remote MDT teaching intervention are shown in Fig. 1.

Fig. 1.

Study design and workflow of the remote MDT teaching intervention

Participants were professional master’s students enrolled between 2022 and 2024 who rotated through the urology department during the study period. A total of 76 eligible students were included. Eligibility was confirmed and baseline information was collected before group assignment. A random number table was used to assign students in a 1:1 ratio to the remote MDT teaching group or the conventional teaching group, with 38 students in each group. Baseline variables included age, sex, entrance examination score, training direction, and duration of clinical rotation.

The school had already arranged the department rotation schedule for the students before the start of the semester. The students who will rotate to the urology department within the fixed time period have been clearly identified, what made the instructors could not be blinded. The rotation examination and competency evaluation followed predefined criteria. No separate assessor-blinding procedure was implemented; this was considered when interpreting the results.

All participants provided written informed consent before enrolment. Because the study was conducted within a single standardized residency training base, all eligible professional master’s students rotating through the department during the study period were considered for inclusion. The final sample size therefore reflected the available trainee cohort rather than an a priori sample size calculation. Given the exploratory nature of this educational intervention, the study was designed to provide an initial estimate of effect across multiple learning outcomes and to inform future multicentre studies with formal power calculations.

Educational intervention

Students in the conventional teaching group received routine urology teaching delivered within a single institution. This included structured lectures, ward-based teaching, case discussions, student presentations and surgical teaching. They also participated in MDT case discussions organized within the home institution. This teaching approach mainly emphasized knowledge acquisition and observational learning within a single institutional and disciplinary framework.

Students in the remote MDT group received the same routine teaching as the conventional teaching group and additionally participated in a structured remote multidisciplinary case discussion every two weeks. These sessions were jointly organized by experts from multiple institutions and specialties. The cases selected for remote MDT discussion were complex urological cases requiring integrative clinical decision-making, such as advanced malignant tumours requiring coordinated input from urology, oncology, radiotherapy and pathology.

The remote MDT teaching model was organized as a three-stage cycle consisting of preparation, discussion and reflection. Before each session, students reviewed case materials through an online platform, searched relevant guidelines and literature, and drafted preliminary diagnostic and treatment plans. During the session, which lasted 90–120 min, students participated in case presentation, structured multidisciplinary discussion, reasoning-based debate and expert feedback. After the session, students completed a written summary, which was reviewed by faculty members. They then engaged in reflective learning based on expert feedback, forming an iterative learning cycle.

In the remote MDT arm, AI-supported functions were used as a preparation and review aid under faculty control. Trainees did not work directly with unedited AI outputs. Before each session, faculty entered de-identified case keywords, diagnostic questions, and planned management topics into the hospital’s DeepSeek-based HtEMRS V3.0 system and the online teaching platform. The system was used to search related literature and guidelines, summarize key evidence, list competing treatment options, and draft questions for discussion. Faculty then checked the citations and clinical statements, removed inaccurate or irrelevant content, and prepared the final case packet, evidence summary, and mind map. During the MDT meeting, these materials were used to ask trainees why they supported one option over another, how guideline recommendations applied to the case, and what additional information from imaging, pathology, oncology, or radiotherapy would change the plan. AI-generated wording was not treated as an answer key. After the meeting, faculty reviewed the AI-assisted summary and mind map again, corrected omissions, and posted the revised summary for trainee reflection. Students could read these faculty-curated materials through the online platform and HtEMRS V3.0, but they were not asked to use raw AI output for grading, real-patient decisions, or independent clinical judgment. The study therefore evaluated a remote MDT teaching package that included faculty-checked AI support; it did not test AI as a separate intervention. In this design, students used faculty-curated AI-informed materials rather than directly operating the AI tools or receiving unedited AI-generated outputs. A summary of the teaching framework is provided in Table 1.

Table 1.

Comparison of MDT teaching content between the remote MDT group and the conventional teaching group

Teaching framework	Remote MDT teaching group	Conventional teaching group
Case selection	Complex urology cases requiring input from at least three disciplines, such as urology, oncology, radiotherapy and pathology.	Routine urology cases discussed within the home institution.
Pre-class preparation	Faculty lectures and textbook self-study; trainees reviewed online case materials through HtEMRS V3.0 and submitted preliminary plans and questions.	Faculty lectures and textbook-based self-study within the home institution.
MDT discussion	Case presentation via Tencent Meeting and HtEMRS V3.0; real-time discussion by experts from three hospitals; trainee Q&A; faculty-checked literature and guideline notes used to support questioning and option comparison.	Home-institution case presentation and MDT discussion; trainees mainly observed and raised questions.
Feedback mechanism	Expert summary; faculty-checked mind map and literature package; trainee written summary; online commentary and Q&A.	Trainee questions followed by oral feedback from faculty members.

Outcome measures

A dual-track assessment strategy combining questionnaire-based evaluation and performance-based assessment was used to evaluate learning outcomes.

The questionnaire component assessed students’ attitudes toward the teaching model, learning experience and perceived educational value using a five-point Likert scale, with higher scores indicating more positive evaluations. The between-group teaching model evaluation score was derived from common questionnaire items completed by students in both groups and was converted to a 100-point scale for analysis. In addition, item-level responses to the remote MDT-specific questionnaire were summarized descriptively and are provided in Supplementary Table 1. The questionnaire covered domains including case preparation, multidisciplinary discussion, expert analysis, online communication, post-session learning value, practicality, innovativeness and overall satisfaction.

Teaching effectiveness was further assessed through rotation examinations and an overall competency evaluation. The rotation examination included three domains: theoretical knowledge, clinical decision-making and practical skills. Theoretical knowledge was assessed using a standardized closed-book examination. Clinical decision-making was assessed through simulated cases, with scoring based on management planning and the reasoning process. Practical skills were evaluated through simulated procedures and oral questioning.

Overall competency was assessed across four equally weighted domains: clinical reasoning, teamwork, remote communication and innovative thinking. Each domain was rated using a five-point scale. This combined approach allowed the study to capture both measurable performance outcomes and learners’ subjective perceptions of the intervention. The competency assessment domains are shown in Table 2.

Table 2.

Competency assessment domains

Domain	Assessment content
Clinical Reasoning	The ability to analyze complex cases from multiple dimensions (e.g., making clinical decisions by integrating tumor TNM staging with genetic testing results).
Team Collaboration	Efficiency of interdisciplinary communication (e.g. formulating precise consultation requests to the imaging department).
Communication Skills	Clarity of expression and responsiveness on online platforms.
Innovative Thinking	Proposing solutions beyond standard protocols (e.g., managing rare complications).

Quality control

Several measures were taken to ensure consistency of implementation. Students in both groups were required to complete all scheduled multidisciplinary teaching activities during the study period. Each remote MDT session included online participation by at least three experts from different specialties to ensure the cross-disciplinary nature of the intervention.

To reduce differences in clinical exposure, all students were required to encounter a comparable range of urological conditions during training. The designated case spectrum covered six major disease categories: renal cell carcinoma, bladder cancer, prostate cancer, benign prostatic hyperplasia, urinary tract stones and urinary tract infection. All participants completed all study assessments, ensuring data completeness and comparability between groups.

Statistical analysis

All statistical analyses were performed using SPSS version 25.0. Categorical variables were presented as frequencies and percentages, and between-group comparisons were performed using the chi-square test or Fisher’s exact test when appropriate. Continuous variables were presented as mean ± standard deviation. Between-group comparisons of continuous variables were performed using the independent-samples t test. All statistical tests were two-sided. A P value < 0.05 was considered statistically significant.

Results

Baseline characteristics

A total of 76 professional master’s students were included in the study and were randomly assigned to the remote MDT teaching group or the conventional teaching group, with 38 students in each group. Baseline characteristics were comparable between the two groups. No statistically significant differences were observed in age, sex, entrance examination score, training direction, or duration of clinical rotation (all P > 0.05), indicating that the two groups were balanced before the intervention. Detailed baseline characteristics are shown in Table 3.

Table 3.

Baseline characteristics of students

Variable	Remote MDT Teaching Group (Observation Group) (n = 38)	Traditional Teaching Group (Control Group) (n = 38)	P value (two-tailed)
Age (years)	27.02 ± 3.59	26.68 ± 2.51	0.53
Gender			0.99
Male	34	33
Female	4	5
Entrance Theory Score	410.41 ± 18.68	408.57 ± 19.33	0.58
Training Orientation			1.000
Urology	3	3
Non-Urology	35	35
Duration of Clinical Rotation (months)	3.45 ± 0.92	3.32 ± 0.78	0.50

Questionnaire findings

The questionnaire showed acceptable reliability and validity. Cronbach’s α coefficients for all questionnaire domains were above 0.70, indicating good internal consistency. The Kaiser–Meyer–Olkin value was 0.852, and Bartlett’s test of sphericity was significant (P < 0.05), supporting the suitability of the questionnaire data for further analysis.

Students generally reported positive perceptions of the remote MDT teaching model across the evaluated questionnaire items. Most items showed mean scores above 4.0, suggesting favourable student evaluations of case preparation, multidisciplinary discussion, expert analysis, online communication, and post-session learning value. Detailed questionnaire responses are provided in Supplementary Table 1.

Teaching outcomes

The remote MDT group achieved a higher theoretical knowledge score than the conventional teaching group (90.24 ± 8.73 vs. 84.77 ± 8.67; t = 2.74, P = 0.008). A similar pattern was observed for clinical decision-making, with higher scores in the remote MDT group than in the conventional teaching group (92.94 ± 4.07 vs. 89.38 ± 5.95; t = 3.05, P = 0.003). The teaching model evaluation score, converted to a 100-point scale, was also higher in the remote MDT group than in the conventional teaching group (86.49 ± 7.98 vs. 81.43 ± 6.38; t = 3.05, P = 0.003).

For overall competency, the remote MDT group demonstrated higher performance than the conventional teaching group (88.54 ± 7.35 vs. 81.53 ± 6.57; t = 4.39, P < 0.001). Domain-specific evaluation indicated that this advantage was mainly reflected in clinical reasoning, teamwork, and communication-related dimensions. In terms of score distribution, students in the remote MDT group were more frequently concentrated in the higher performance range.

In contrast, no significant difference was found between the two groups in practical skills (85.05 ± 7.31 vs. 84.98 ± 7.23; t = 0.04, P = 0.966). Overall, the remote MDT model showed a stronger association with outcomes involving knowledge integration and decision-making, whereas its effect on procedural skill performance was limited. Detailed teaching outcome results are presented in Table 4.

Table 4.

Comparison of teaching outcomes between groups

Outcome	Remote MDT group, mean	Remote MDT group, SD	Conventional group, mean	Conventional group, SD	t	P value
Practical Operation Skills	85.05	7.31	84.98	7.23	0.04	0.966
Theoretical Test Scores	90.24	8.73	84.77	8.67	2.74	0.008
Comprehensive Competency Assessment	88.54	7.35	81.53	6.57	4.39	< 0.001
Clinical Decision-Making	92.94	4.07	89.38	5.95	3.05	0.003
Model Scale Scores	86.49	7.98	81.43	6.38	3.05	0.003

Discussion

Contemporary clinical practice requires physicians not only to master foundational knowledge, but also to work with uncertainty, integrate heterogeneous information and collaborate across disciplines [5, 19–21]. Traditional lecture-based teaching remains useful for standardized knowledge delivery, but it is less suited to cultivating higher-order abilities such as integrative reasoning, clinical judgment and decision-making in uncertain situations [3, 19, 20, 22]. This gap is particularly relevant in postgraduate education, where learners are expected to move beyond knowledge acquisition and begin to form independent clinical judgments. The present study therefore examined whether a structured remote MDT teaching model could better support these learning needs in urology postgraduate training, building on emerging evidence regarding MDT-based and urology-related case-based teaching models [23, 24].

The findings showed that students in the remote MDT group achieved higher scores in theoretical knowledge, clinical decision-making, questionnaire-based evaluation and overall competency than those in the conventional teaching group. No significant difference was observed in practical skills. This pattern suggests that the remote MDT model may be most effective for learning outcomes that depend on case interpretation, knowledge integration, shared reasoning and collaborative decision-making. By contrast, procedural performance may require a different set of learning conditions, including repeated hands-on practice, direct supervision and immediate correction.

Several features of the intervention may help explain these findings. First, the remote MDT model was not simply a transfer of teaching from an offline to an online format. It was a structured teaching package that combined pre-session preparation, remote multidisciplinary discussion, expert-guided case analysis, post-session reflection and iterative feedback. Such a design may have created repeated opportunities for learners to review clinical information, formulate preliminary judgments, compare alternative perspectives and refine their reasoning after expert feedback. From the perspective of instructional design, the benefit of the intervention is therefore unlikely to be attributable to remote delivery alone. It is more plausibly related to the way in which the different instructional components were sequenced and integrated [25, 26].

Second, MDT-based learning may make clinical reasoning more visible. In routine workplace teaching, clinical reasoning is often implicit: learners may observe decisions without clearly understanding how information is selected, weighted and interpreted. In the remote MDT sessions, students were required to prepare cases, participate in discussion and revisit the clinical problem after the session. This process may have helped transform reasoning from an internal expert process into an observable and discussable learning activity, which is consistent with social constructivist, situated learning and cognitive apprenticeship perspectives [13–15]. This interpretation is consistent with work on clinical reasoning curricula, which emphasizes the need for case-based practice, structured reflection and feedback rather than reliance on clinical exposure alone [22]. It is also consistent with research on collaborative clinical reasoning and with recent conceptual work viewing MDT meetings as potential team-learning activities [27].

Third, the remote format may have expanded the learning environment beyond the limits of a single institution. Urology postgraduate education often depends on the local availability of complex cases and specialist faculty. A remote MDT platform can connect trainees with experts and cases from multiple centres, thereby broadening their exposure to diagnostic and therapeutic reasoning. This may be particularly valuable for complex urological malignancies, where decisions frequently require the integration of surgical, oncological, radiotherapeutic and pathological perspectives [6, 21]. The educational value of this format lies not only in access to more information, but also in access to different ways of framing and solving the same clinical problem.

As Katsimperis S. et al. mentioned AI in urology training have practical benefits and real-world limits [17]. First, the practical benefit is enhance the efficiency of learning. Second, we should realize the limitaions in real-world, such as the judgment of AI is based on the input of human knowledge with a certain error rate. In our study, AI was be interpreted as one practical support within the teaching workflow and Its main contribution was to help faculty prepare and standardize the materials used in remote MDT sessions. In complex urological cases, relevant information is spread across guidelines, imaging reports, pathology findings, oncological treatment options, radiotherapy considerations and surgical planning. In our study, by using AI- supported retrieval and summarization before class, faculty could prepare evidence notes, comparison prompts, and visual mind maps more efficiently. During discussion, these reviewed materials helped students explain their reasoning, compare options, and identify what evidence or specialist input was still needed. From the trainee perspective, the value of these materials was not to provide ready-made answers, but to help them enter the case more quickly, check guideline-based evidence, organize questions, and respond to faculty feedback during MDT discussion. Because the same reviewed case packets, evidence notes, question lists, and corrected summaries could be shared on the remote platform, this workflow may also help reduce differences in educational exposure between trainees at high-level centres and those at the local institution. After discussion, corrected summaries gave students a concise record for review. This use is consistent with the observed improvement in theoretical knowledge, clinical decision-making, and overall competency. However, it also explains why practical skills did not improve: AI-prepared materials and online MDT discussion can support reasoning, but they cannot provide physical demonstration, repeated procedural practice, or real-time technical correction. Therefore, we cautiously regard AI as a supporting measure in the MDT process: teachers used AI to prepare and review materials, while students used the faculty-curated AI-informed materials rather than directly operating the AI tools or receiving unedited AI outputs.

The lack of improvement in practical skills is also important. In the present study, the remote MDT intervention was mainly a non-operative educational activity, including case presentation, expression of diagnostic and therapeutic opinions, evidence discussion, option comparison, multidisciplinary reasoning, and written reflection. It did not include direct endoscopic, laparoscopic, or bedside operation, live observation of students’ hand movements, structured technical scoring, or immediate correction. Procedural competence depends on direct performance, repeated practice, physical demonstration, immediate correction, and situated feedback [2, 28]. These conditions cannot be fully replaced by online discussion or remote case review. Remote MDT teaching should therefore be regarded as a complementary part of postgraduate urology training, with particular value for reasoning, decision-making, and collaboration, rather than a substitute for hands-on procedural teaching. Future curricula could combine remote MDT sessions with endoscopic or laparoscopic simulation, supervised bedside or operating-room teaching, objective assessment of technical skills, and hands-on workshops. For example, trainees could first perform endoscopic or laparoscopic simulation in a local skills laboratory while the procedure is livestreamed to remote MDT faculty or recorded for later review. Remote experts and local supervisors could use a shared checklist or objective structured technical assessment to score instrument handling, respect for tissue, safety, anatomical exposure, procedural sequence, and completion of key steps. Errors could then be paused, replayed, discussed, and corrected by local supervisors, followed by repeated practice and review of uploaded videos, scores, and correction points in the next MDT session or hands-on workshop. Through these various approaches, we hope that remote MDT teaching can better be integrated with simulation-based training, supervisory teaching, or practical workshops, in order to facilitate the improvement of practical skills.

The study has several limitations. First, it was conducted in a single standardized residency training base with a relatively small sample. The sample size was mainly determined by the available cohort of eligible postgraduate trainees during the study period, rather than by a formal a priori power calculation. The findings should therefore be interpreted cautiously and confirmed in adequately powered multicentre studies. In future research, we will calculate the sample size in advance, obtain a sufficient cohort of trainees, and reduce the statistical differences caused by an insufficient sample size. Second, Since the student rotation schedule is fixed, this study cannot be conducted in a blinded manner. In future research, we will address this drawback and consider new groupings, such as randomly numbering students, experts, and teachers. Each remote MDT session will only display the numbers, and the specific names corresponding to those numbers will only be known to the researchers. Third, AI-supported functions were used only as faculty-controlled aids for literature retrieval, case summarization, question generation, and post-session summaries. We did not record prompts, AI outputs, faculty edits, or how often trainees used the reviewed materials. Therefore, we cannot quantify the independent contribution of AI or the accuracy of each AI-assisted output. Future studies should compare remote MDT teaching with and without AI-supported preparation, audit the original and faculty-edited outputs, and measure trainee use of these materials. Fourth, longer-term outcomes, such as retention of clinical reasoning ability, transfer to workplace performance, and effects on patient-related decision-making, were not examined.

Conclusion

This randomized controlled study suggests that remote MDT teaching may improve selected dimensions of urology postgraduate education, particularly theoretical knowledge, clinical decision-making and overall competency. The lack of improvement in practical skills indicates that the model is better suited to outcomes involving reasoning, knowledge integration and collaborative decision-making than to procedural performance.

The educational value of the remote MDT model appears to lie in the structured organization of learning rather than in remote delivery alone. By combining case preparation, multidisciplinary discussion, expert-guided analysis, reflection and feedback, the model may help make clinical reasoning more visible and more accessible to postgraduate learners. Remote MDT teaching should therefore be considered a complementary approach within clinical training, to be integrated with supervised hands-on practice and skills-based teaching.

Authors' contributions

W .Z.: Writing – original draft, Methodology, Investigation, Data curation, Formal analysis.H. L.: Writing – original draft, Methodology, Investigation, Data curation, Visualization.Y. G.: Conceptualization, Supervision, Project administration, Writing – review &amp; editing.S. X.: Investigation, Data curation, Validation.Jiaqian Yan: Investigation, Data curation.Z. L.: Investigation, Resources.Q. H.: Supervision, Funding acquisition, Writing – review &amp; editing.Y. L.: Supervision, Funding acquisition.W. H.: Resources, Technical support.

Funding

This study was supported by the Postgraduate Education and Teaching Project of Fujian Medical University (Project No. Y24014 and YT25008).

Data availability

The datasets generated and analysed during the current study are not publicly available because they contain educational assessment data from a small cohort of postgraduate trainees and may include information that could compromise participant privacy. De-identified data may be made available from the corresponding author upon reasonable request, subject to approval by the relevant institutional ethics committee and compliance with applicable privacy and data protection regulations.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of The Second Affiliated Hospital of Fujian Medical University (No. 2025 − 780). The study was conducted in accordance with the Declaration of Helsinki. All participants were informed of the purpose and procedures of the study, and written informed consent was obtained before enrolment. Participation was voluntary.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.