Evaluation of an Artificial Intelligence-enabled Clinical Assistant to Support Thyroid Cancer Management
July 17, 2026 updated by: Dr. Carlos King-Ho Wong, The University of Hong Kong
A Randomized Controlled Trial to Evaluate an Artificial Intelligence-enabled Clinical Assistant Leveraging Large Language Models for Thyroid Cancer Staging and Risk Stratification Among Medical Students and Clinicians
This study aims to evaluate the clinical feasibility of adopting artificial intelligence (AI)-based models to improve clinical management of thyroid cancer.
Study Overview
Status
Enrolling by invitation
Conditions
Intervention / Treatment
Detailed Description
With recent advancements in technology, AI has become widely applicable to visual text recognition in clinical settings.
AI-powered text recognition is emerging as a highly efficient, sustainable, and cost-effective tool for decision making and personalised medicine.
Numerous studies have employed natural language processing (NLP) algorithms, particularly large language models (LLMs), to convert unstructured free-text from clinical consultation notes within electronic health records (EHR) into structured data, thus enriching individual clinical profiles in the EHR databases.
Over time, these AI models have continuously improved their predictive accuracy and performance through self-learning (or unsupervised learning).
While AI models had made a significant impact in oncology practices overseas, their utility for text recognition in oncology remains limited in Hong Kong.
This proposed study aims to evaluate the clinical feasibility of adopting AI-based models to improve time efficiency, accuracy, and end-users' confidence in diagnostic assessment and risk prediction, compared against traditional workflows without AI assistant for thyroid cancer management.
Study Type
Interventional
Enrollment (Estimated)
76
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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-
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Hong Kong, Hong Kong
- School of Public Health, The University of Hong Kong
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Hong Kong, Hong Kong
- Department of Surgery, School of Clinical Medicine, The University of Hong Kong
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Yes
Description
Inclusion Criteria:
- Consenting medical students
- Consenting clinicians who are directly involved in the care of thyroid cancer patients, including endocrine surgeons, endocrinologists, oncologists, and pathologists.
Exclusion Criteria:
• Medical students and clinicians who had reviewed the clinical notes or were involved in the processing of the clinical notes prior to the commencement of trial
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Health Services Research
- Allocation: Randomized
- Interventional Model: Crossover Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: AI-enabled clinical assistant
Participants will provide the caner staging and risk category of each thyroid cancer patient as well as the participants' confidence for the above diagnostic assessments with AI-enabled clinical assistant as the intervention.
The AI assistant is powered by LLMs and comprises a clinical dashboard.
The clinical dashboard displays the original clinical notes and summarizes cancer staging and risk category of each thyroid cancer patient generated from the backend processing of the clinical assistant.
Supporting evidence from original clinical notes is also highlighted for participants' verification.
|
Participants will provide the caner staging and risk category of each thyroid cancer patient as well as the participants' confidence for the above diagnostic assessments with AI-enabled clinical assistant as the intervention.
The AI assistant is powered by LLMs and comprises a clinical dashboard.
The clinical dashboard displays the original clinical notes and summarizes cancer staging and risk category of each thyroid cancer patient generated from the backend processing of the clinical assistant.
Supporting evidence from original clinical notes is also highlighted for participants' verification.
|
|
No Intervention: Manural chart review
Participants will provide the caner staging and risk category of each thyroid cancer patient as well as the participants' confidence for the above diagnostic assessments with manual chart review.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Efficiency
Time Frame: Between intervention group and non-intervention group. Cross-over in 4-26 weeks
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The time required to complete reviewing one set of clinical notes is compared between intervention and non-intervention groups
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Between intervention group and non-intervention group. Cross-over in 4-26 weeks
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Accuracy of Cancer Staging and Risk Stratification by Participants Compared with Ground Truth across Intervention and Non-intervention Groups
Time Frame: Between intervention group and non-intervention group. Cross-over in 4-26 weeks
|
The study will compare the accuracy of cancer staging and risk category assessed by the participants across the intervention group with AI assitance and non-intervention group without AI asssitance. The participants will review the clinical notes and assess the cancer staging and risk category for each thyroid cancer patient with or without the AI assistant. Participant provided assessments will be compared against the ground truth established by the clinical investigators of the study to guage the accuracy which is quantified as the percentage of correctly graded cancer staging and risk stratification. The accuracy will be compared between the intervention group and non-intervention groups using t-tests to evaluate the clinical impact of the AI assistant. |
Between intervention group and non-intervention group. Cross-over in 4-26 weeks
|
|
Participants' Confidence in Cancer Staging and Risk Stratification as Assessed by a 0-10 Scale Questionnaire
Time Frame: Between intervention group and non-intervention group. Cross-over in 4-26 weeks
|
The study will compare the participants' confidence in grading cancer staging and risk category between the intervention group with AI assistance and non-intervention group without AI-assistance. After evaluating each thyroid cancer case for providing cancer staging and risk category, participants will complete a short questionnaire rating their confidence in providing their assessments on a scale from 0 (lowest) to 10 (hightest). Meanw confidence score will be compared between the intervention group and non-intervention group to evaluate the clinical impact of the AI assitant. |
Between intervention group and non-intervention group. Cross-over in 4-26 weeks
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Investigators
- Principal Investigator: King Ho Carlos Wong, School of Public Health The University of Hong Kong
- Principal Investigator: Man Him Matrix Fung, Department of Surgery, School of Clinical Medicine, The University of Hong Kong
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
October 2, 2025
Primary Completion (Estimated)
March 31, 2027
Study Completion (Estimated)
April 30, 2027
Study Registration Dates
First Submitted
September 29, 2025
First Submitted That Met QC Criteria
November 16, 2025
First Posted (Actual)
November 18, 2025
Study Record Updates
Last Update Posted (Actual)
July 21, 2026
Last Update Submitted That Met QC Criteria
July 17, 2026
Last Verified
July 1, 2026
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- UW24-319-RCT
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
YES
IPD Plan Description
Only anonymized IPD used in results publications will be shared so that re-identification of individuals is not possible.
IPD Sharing Time Frame
The IPD and supporting information will be available upon the completion of study (anticipated date as 30 April 2027) with results dissemination or publication, and will remain unending until required of removal.
IPD Sharing Access Criteria
The IPD and supporting information will be available with results dissemination and publication as documents uploads or attachment.
Anyone who has access to the articles will be able to access all the documents.
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
- SAP
- ICF
- ANALYTIC_CODE
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
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