Application of the Belle.AI Dermatological Image Reference System on Patient Diagnosis Within an Active Clinical Setting
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 263
- 试验地点
- 2
- 主要终点
- Accuracy of Belle AI Image References
研究概览
简要总结
The goal of this research study is to test a new, investigational tool that uses artificial intelligence (AI) to help primary care providers assess skin conditions. This tool is an AI-powered dermatology image reference app that works with a smartphone. For clarity, the AI makes no diagnoses; it provides reference images. Primary care providers then use their own medical judgement and training to make the diagnosis.
The sponsor aims to compare the diagnoses made by primary care providers (such as doctors, nurse practitioners, and physician assistants) with the support of the AI tool compared to a panel of dermatologists, who are setting the gold standard. By doing so, the sponsor can determine the value of the AI tool for primary care providers and understand how it might be used alongside traditional clinical care.
This AI capability complies with FDA regulatory guidelines and is not considered a medical device, similar to a Google image search, which returns similar looking images for reference purposes. For intervention, they healthcare providers use their own training and clinical judgement to make the diagnosis, and not the AI.
详细描述
Access to dermatologists is often limited, leading to around 60% of skin, hair, and nail issues being treated by non-specialists. This study will evaluate the effectiveness of an AI dermatology decision support tool in assisting primary care providers (PCPs) with the diagnosis of skin conditions. AI-based image analysis has been shown to enhance diagnostic accuracy, particularly for non-dermatologists. Previous studies have primarily focused on dermatologists, but AI could be more beneficial for PCPs, as it has been shown to improve their diagnostic accuracy and agreement with dermatologists.
Globally, about 1.9 billion people suffer from skin diseases annually, with 1 in 3 Americans seeking dermatological care from non-specialists. Skin-related issues make up a significant proportion of visits to general practitioners and emergency departments. AI has proven effective in diagnosing skin conditions such as melanoma and other inflammatory diseases, and studies indicate that AI tools can enhance diagnostic accuracy, particularly for non-dermatologists.
The Belle AI tool, which will be used in this study, employs a convolutional neural network trained on over 500,000 images to identify over 2,000 skin conditions. It provides image match scores to help physicians identify conditions and offers a protocol for second opinions from board-certified dermatologists. The study aims to assess the tool's utility in real-time clinical settings, with potential improvements in triage accuracy, referral quality, and cost savings.
This study is supported by the Advanced Research Projects Agency for Health (ARPA-H) and will be one of the first to prospectively examine AI's impact on dermatology decision support in primary care.
The study aims to evaluate the accuracy and utility of the Belle AI dermatological reference system in a real-world clinical environment, in partnership with Urban Health Plan (UHP). Key endpoints include assessing diagnostic utility and accuracy compared to a final diagnosis from a dermatological review committee, as well as gathering feedback from primary care providers and physician extenders on their experiences with the AI. The sponsor will also analyze the cost implications of the system's use to demonstrate its value in frontline medicine.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patient must present to walk-in clinic with a primary dermatological complaint.
- •Patient must have the ability and willingness to provide informed consent and comply with study procedures and visits.
- •Participants must have access to the required technology (e.g., smartphone with internet access) and be capable of using it for the required image capture.
排除标准
- •Patients who are unable to comply with study procedures due to physical or mental health limitations (as assessed by study coordinator).
- •Pediatric, adolescent, and teen patients who present with dermatological conditions on their genitalia will not be included in the study (in support of patient privacy concerns).
结局指标
主要结局
Accuracy of Belle AI Image References
时间窗: Through study completion, an average of 14 days
Comparison of Belle AI's image reference system primary probability diagnosis to the reference diagnosis established by the dermatological review committee; additional evaluation of the accuracy of the second and third highest probability diagnoses determined by the Bell Image Match Score
Impact on Economic Burden
时间窗: Through study completion, an average of 14 days
Measure economic impact by enabling remote assessment for patients
次要结局
- Agreement between diagnoses(Through study completion, an average of 14 days)
- Provider-Rated Usefulness(Through study completion, an average of 14 days)
- Cost-Impact Analysis(Through study completion, an average of 14 days)