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| Name | Class |
|---|---|
| Innovation and Technology Commission, Hong Kong | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| 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 |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Surgery, School of Clinical Medicine, The University of Hong Kong | Hong Kong | Hong Kong | ||||
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40025285 | Result | 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. |
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Only anonymized IPD used in results publications will be shared so that re-identification of individuals is not possible.
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.
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.
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| ID | Term |
|---|---|
| D013964 | Thyroid Neoplasms |
| ID | Term |
|---|---|
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D006258 | Head and Neck Neoplasms |
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| School of Public Health, The University of Hong Kong |
| Hong Kong |
| Hong Kong |
| D004700 |
| Endocrine System Diseases |
| D013959 | Thyroid Diseases |