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

NCT ID: NCT07234539

Last Updated: 2025-12-17

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Clinical Phase

NA

Total Enrollment

70 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-10-02

Study Completion Date

2026-04-30

Brief Summary

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

Detailed Description

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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 the end-user confidence in diagnostic accuracy and risk prediction using AI-assisted workflows in thyroid cancer management.

Conditions

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Thyroid Cancer Large Language Models

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

SINGLE

Outcome Assessors

Study Groups

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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.

Group Type EXPERIMENTAL

AI-enabled clinical assistant

Intervention Type 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.

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.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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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.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* medical students
* clinicians (including but not limited to surgeons, oncologists, 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 clinical experimental studies
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Innovation and Technology Commission, Hong Kong

OTHER

Sponsor Role collaborator

The University of Hong Kong

OTHER

Sponsor Role lead

Responsible Party

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Dr. Carlos King-Ho Wong

Honorary Associate Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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King Ho Carlos Wong

Role: PRINCIPAL_INVESTIGATOR

School of Public Health The University of Hong Kong

Man Him Matrix Fung

Role: PRINCIPAL_INVESTIGATOR

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

Locations

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

Hong Kong, , Hong Kong

Site Status

School of Public Health, The University of Hong Kong

Hong Kong, , Hong Kong

Site Status

Countries

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Hong Kong

References

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Murphy GS, Szokol JW, Marymont JH, Avram MJ, Vender JS, Rosengart TK. Impact of shorter-acting neuromuscular blocking agents on fast-track recovery of the cardiac surgical patient. Anesthesiology. 2002 Mar;96(3):600-6. doi: 10.1097/00000542-200203000-00015.

Reference Type RESULT
PMID: 11873034 (View on PubMed)

Other Identifiers

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UW24-319-RCT

Identifier Type: -

Identifier Source: org_study_id