A Multi-Reader Multi-Case Study for Evaluating the Impact of Legit.Health Plus Device on the Healthcare Practitioners' Assessment of Skin Lesions
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 16
- 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 doctors more accurately identify a wide variety of skin conditions and improve the efficiency of patient consultations. While many patients visit primary care for skin issues, general doctors may sometimes have different opinions from specialists, which can lead to delays in getting the right treatment.
The researchers hypothesized that using the AI tool would increase the true diagnostic accuracy of healthcare professionals for multiple skin conditions. To test this, 16 doctors (including 10 general practitioners and 6 dermatologists) evaluated 29 different medical images.
For each case, the doctors followed a structured process:
- Initial Assessment: Doctors first gave a diagnosis based only on the patient's image and medical history.
- AI Support: Doctors were then shown the AI's top five suggested diagnoses and confidence levels to see if they wished to adjust their final decision.
- Clinical Utility: Doctors also indicated if the patient required a specialist referral and if the case could be handled through a remote (online) consultation.
The primary question the study tried to answer was whether AI support could significantly improve correct diagnoses across 13 different types of skin pathologies-ranging from common rashes to skin cancer-while also making the consultation process faster and more effective for both doctors and patients.
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 and dermatology.
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 evaluate the impact of Computer-Aided Diagnosis (CAD) on clinician performance.
- Self-Controlled Framework: Each healthcare professional (HCP) served as their own comparator, providing diagnoses first without the use of the device and subsequently with the support of the device on the same set of images.
- Evaluation Workflow: Participants accessed a secure web-based platform to review 29 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 International Classification of Diseases (ICD) categories and confidence levels.
- Clinical Utility Assessment: The study included a specific questionnaire to evaluate the utility of the data, consultation time reductions, and confidence in making remote clinical decisions.
- Pathology Diversity: The dataset included 13 distinct skin conditions, ranging from common ailments like Acne and Dermatitis to malignant conditions such as Melanoma and Basal Cell Carcinoma.
Quality Assurance and Data Management
To ensure the scientific integrity of the 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 practitioners and dermatologists, 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
Healthcare Professionals (Primary Care Physicians and Dermatologists)
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. - Dual Professional Roles: The group includes 10 primary care physicians (PCPs) and 6 dermatologists, allowing for a comparison between generalist and specialist 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 Dermatology (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 healthcare professionals (HCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI device's top 5 suggestions and confidence levels-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)
- Clinical Utility and Usability Scores for Diagnostic Support.(Day 1)