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

Официальное название

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

Обзор

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

Подробное описание

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.

Вмешательства

• Другое 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

Первичные конечные точки

• Efficiency [Срок оценки: Between intervention group and non-intervention group. Cross-over in 4-26 weeks]

Вторичные конечные точки (2)

• Accuracy of Cancer Staging and Risk Stratification by Participants Compared with Ground Truth across Intervention and Non-intervention Groups [Срок оценки: 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 [Срок оценки: Between intervention group and non-intervention group. Cross-over in 4-26 weeks]

Критерии участия

Критерии включения

• Consenting medical students
• Consenting clinicians who are directly involved in the care of thyroid cancer patients, including endocrine surgeons, endocrinologists, oncologists, and pathologists.

Критерии исключения

• 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Перекрёстный дизайн

Маскирование

Простое слепое

Основная цель

Организация здравоохранения

Центры проведения

Гонконг · 2 центра

• Department of Surgery, School of Clinical Medicine, The University of Hong Kong — Гонконг
• School of Public Health, The University of Hong Kong — Гонконг

Публикации

• 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

Идентификаторы

NCT: NCT07234539 · UW24-319-RCT

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗