A Multi-Reader Multi-Case (MRMC) Study Assessing the Impact of Legit.Health Plus on the Diagnostic Accuracy and Referral Decision-Making of Primary Care Physicians for Skin Lesions.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 9
- 试验地点
- 1
- 主要终点
- Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.
研究概览
简要总结
This study aims to determine if an artificial intelligence (AI) medical device can help primary care doctors more accurately identify and manage various skin conditions. Skin issues are a frequent reason for doctor visits, but differences in expertise between general practitioners and specialists can sometimes lead to misdiagnoses or unnecessary referrals.
The researchers hypothesized that the information provided by the AI device would increase the true diagnostic accuracy of primary care practitioners for multiple dermatological conditions. To test this, the study followed a prospective, self-controlled design where each participating doctor served as their own comparison.
During the study, 9 primary care physicians evaluated 30 clinical images representing a variety of skin pathologies. For each image, the doctors followed a two-step process:
- First, they provided a diagnosis based only on the image and the patient's medical history.
- Second, they were shown the AI's analysis-including the top 5 suggested diagnoses and confidence levels-and asked to provide a final diagnosis.
The study also investigated if the AI could help doctors decide whether a patient truly needs a referral to a specialist or if the condition could be handled remotely via teledermatology. The primary question was whether using this AI support would significantly increase the number of correct diagnoses made by primary care doctors and lead to more efficient patient care.
详细描述
This detailed description outlines the clinical methodology, technical framework, and data integrity protocols utilized in the investigation of the Legit Health Plus medical device for skin pathologies in primary care.
Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional self-controlled study. It utilized a Multi-Reader Multi-Case (MRMC) framework to measure the impact of Computer-Aided Diagnosis (CAD) on clinician performance.
- Self-Controlled Framework: Each primary care practitioner (PCP) served as their own comparator, providing diagnoses first without and then with device support.
- Sequential Evaluation Workflow: Participants accessed a secure web-based platform to review 30 clinical cases. For each case, doctors provided an initial diagnosis based on an image and medical history, followed by a final diagnosis after reviewing the AI's top 5 suggested ICD-11 categories and confidence levels.
- Clinical Decision Support: The study also evaluated clinician decisions regarding dermatology referrals and the feasibility of remote management (teledermatology) based on AI-provided data, such as malignancy indices.
- Case Distribution: The 30 clinical images represented nine different conditions, including Melanoma, Basal Cell Carcinoma, Psoriasis, and Hidradenitis Suppurativa, all previously confirmed by dermatologists and anatomical pathology.
Quality Assurance and Data Management
To ensure the scientific integrity of the clinical investigation, the following quality and monitoring protocols were implemented:
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Board-certified primary care physicians regardless of their professional experience.
- •High-quality images of patients with different skin conditions.
排除标准
- •Low-quality images of patients which can not be properly analyzed.
研究组 & 干预措施
Primary Care Physicians
This group is composed of board-certified healthcare professionals (HCPs) who serve as the "readers" in this multi-reader multi-case (MRMC) study. The cohort is uniquely characterized by its internal comparison: each participant acts as their own control.
- The group includes 9 primary care physicians (PCPs), allowing for a comparison of PCPs diagnostic baseline performance.
- Interventional Exposure: All participants are evaluated under two distinct conditions: first, providing a diagnosis based solely on clinical images and patient history; second, providing a diagnosis assisted by the AI-based medical device's top 5 suggestions and confidence levels.
- Clinical Expertise: Every member of the cohort has a minimum of 5 years of clinical experience in their respective field.
干预措施: AI-based medical device for aided diagnosis in dermatological conditions (Device)
结局指标
主要结局
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.
时间窗: Day 1
This measure evaluates the "Top-1" diagnostic accuracy of primary care practitioners (PCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI's top 5 suggestions-against a confirmed reference standard (confirmed by dermatologists or anatomical pathology).
次要结局
- Change in Dermatology Referral Rate Assisted by Artificial Intelligence.(Day 1)
- Percentage of Cases Deemed Manageable via Remote Consultation.(Day 1)