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

The University of Hong Kong (HKU)

Status: Invitation-only

Conditions: Thyroid Cancer; Large Language Models

Treatments: Other: AI-enabled clinical assistant

Study type: Interventional

Funder types: Other

Identifiers

NCT07234539

UW24-319-RCT

Details and patient eligibility

About

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

Full 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.

Enrollment: 70 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: Single Blind

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70 participants in 2 patient groups

AI-enabled clinical assistant

Experimental group

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.

Treatment:

Other: AI-enabled clinical assistant

Manural chart review

No Intervention group

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.