Clinical trial · Interventional
Evaluation of an Artificial Intelligence-enabled Clinical Assistant to Support Thyroid Cancer Management
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
NCT07234539CI-TRIAL-00118736enrolling by invitationN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study aims to evaluate the clinical feasibility of adopting artificial intelligence (AI)-based models to improve clinical management of thyroid cancer.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Large Language Models | — | UNRESOLVED | — |
| Thyroid Cancer | Malignant Thyroid Gland Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-enabled clinical assistant | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- AI-enabled clinical assistant
- description
- 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.
- interventionNames
- Other: AI-enabled clinical assistant
- type
- NO_INTERVENTION
- label
- Manural chart review
- description
- 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.
Primary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (1)
- RESULTFung 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