Evaluation of an Artificial Intelligence-enabled Clinical Assistant to Support Thyroid Cancer Management
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-enabled clinical assistant.
- Who it may be relevant to
- Registry conditions: Thyroid Cancer, Large Language Models. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Hong Kong
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
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
Overview
This study aims to evaluate the clinical feasibility of adopting artificial intelligence (AI)-based models to improve clinical management of thyroid cancer.
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.
Interventions
- Other 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
Primary outcome measures
- Efficiency [Time frame: Between intervention group and non-intervention group. Cross-over in 4-26 weeks]
Secondary outcome measures (2)
- 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]
- 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]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Crossover
- Masking
- Single blind
- Primary purpose
- Health services research
Study locations
Hong Kong · 2 centers
- Department of Surgery, School of Clinical Medicine, The University of Hong Kong — Hong Kong
- School of Public Health, The University of Hong Kong — Hong Kong
Publications
- 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
Identifiers
NCT: NCT07234539 · UW24-319-RCT