Evaluation of an Artificial Intelligence-enabled Clinical Assistant to Support Thyroid Cancer Management

NCT07234539 | Enrolling By Invitation

• 1 other identifier: UW24-319-RCT
• Study Type: interventional
• Target: 70
• Locations: 1 country
• Sites: 2

Brief Summary

This study aims to evaluate the clinical feasibility of adopting artificial intelligence (AI)-based models to improve clinical management of thyroid cancer.

Conditions

Thyroid Cancer; Large Language Models

Keywords

Thyroid Cancer; large language models; LLMs; natural language processing; NLP

Outcome Measures

Primary Outcomes (3)

• Accuracy of Cancer Staging and Risk Stratification by Participants Compared with Ground Truth across Intervention and Non-intervention Groups

  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 3-4 weeks

• Participants' Confidence in Cancer Staging and Risk Stratification as Assessed by a 0-10 Scale Questionnaire

  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 3-4 weeks

• Efficiency

  The time required to complete reviewing one set of clinical notes is compared between intervention and non-intervention groups

  Between intervention group and non-intervention group. Cross-over in 3-4 weeks

Study Arms (2)

AI-enabled clinical assistant

EXPERIMENTAL

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.

Other: AI-enabled clinical assistant

Manural chart review

NO INTERVENTION

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.

Interventions

AI-enabled clinical assistant OTHER

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.

AI-enabled clinical assistant

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• medical students
• clinicians (including but not limited to surgeons, oncologists, pathologists)

You may not qualify if:

• 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 clinical experimental studies

Sponsors & Collaborators

• The University of Hong Kong: lead
• Innovation and Technology Commission, Hong Kong: collaborator

Study Sites (2)

Department of Surgery, School of Clinical Medicine, The University of Hong Kong

Hong Kong, Hong Kong

Location

School of Public Health, The University of Hong Kong

Hong Kong, Hong Kong

Location

Related Publications (1)

• Fung MMH, Tang EHM, Wu T, Luk Y, Au ICH, Liu X, Lee VHF, Wong CK, Wei Z, Cheng WY, Tai ICY, Ho JWK, Wong JWH, Lang BHH, Leung KSM, Wong ZSY, Wu JT, Wong CKH. Developing a named entity framework for thyroid cancer staging and risk level classification using large language models. NPJ Digit Med. 2025 Mar 1;8(1):134. doi: 10.1038/s41746-025-01528-y.

  PMID: 40025285 RESULT

MeSH Terms

Conditions

Thyroid Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Head and Neck Neoplasms; Endocrine System Diseases; Thyroid Diseases

Study Officials

• 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

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Honorary Associate Professor

Study Record Dates

• First Submitted: September 29, 2025
• First Posted: November 18, 2025
• Study Start: October 2, 2025
• Primary Completion: March 31, 2026
• Study Completion: April 30, 2026
• Last Updated: December 17, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, ANALYTIC CODE
• Time Frame: The IPD and supporting information will be available upon the completion of study (anticipated date as 31 December 2025) with results dissemination or publication, and will remain unending until required of removal.
• 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.

Locations

HK (2)