Evaluation of an Artificial Intelligence-enabled Clinical Assistant to Support Thyroid Cancer Management
- Conditions
- Thyroid CancerLarge Language Models
- Registration Number
- NCT07234539
- Lead Sponsor
- The University of Hong Kong
- Brief Summary
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 the end-user confidence in diagnostic accuracy and risk prediction using AI-assisted workflows in thyroid cancer management.
Recruitment & Eligibility
- Status
- ENROLLING_BY_INVITATION
- Sex
- All
- Target Recruitment
- 70
- medical students
- clinicians (including but not limited to surgeons, oncologists, pathologists)
- 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
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- CROSSOVER
- Primary Outcome Measures
Name Time Method Accuracy of Cancer Staging and Risk Stratification by Participants Compared with Ground Truth across Intervention and Non-intervention Groups Between intervention group and non-intervention group. Cross-over in 3-4 weeks 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.Participants' Confidence in Cancer Staging and Risk Stratification as Assessed by a 0-10 Scale Questionnaire Between intervention group and non-intervention group. Cross-over in 3-4 weeks 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.Efficiency Between intervention group and non-intervention group. Cross-over in 3-4 weeks The time required to complete reviewing one set of clinical notes is compared between intervention and non-intervention groups
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (2)
Department of Surgery, School of Clinical Medicine, The University of Hong Kong
🇭🇰Hong Kong, Hong Kong
School of Public Health, The University of Hong Kong
🇭🇰Hong Kong, Hong Kong
Department of Surgery, School of Clinical Medicine, The University of Hong Kong🇭🇰Hong Kong, Hong Kong