Pankow Anne, Meißner-Bendzko Nico, Kaufeld Jessica, Fouquette Laura, Cotte Fabienne, Gilbert Stephen, Türk Ewelina, Das Anibh, Terkamp Christoph, Burmester Gerhard-Rüdiger, Wagner Annette Doris
Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Department of Gastroneterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School, Hannover, Germany.
JMIR AI. 2025 Aug 28;4:e55001. doi: 10.2196/55001.
Rare diseases, which affect millions of people worldwide, pose a major challenge, as it often takes years before an accurate diagnosis can be made. This delay results in substantial burdens for patients and health care systems, as misdiagnoses lead to inadequate treatment and increased costs. Artificial intelligence (AI)-powered symptom checkers (SCs) present an opportunity to flag rare diseases earlier in the diagnostic work-up. However, these tools are primarily based on published literature, which often contains incomplete data on rare diseases, resulting in compromised diagnostic accuracy. Integrating expert interview insights into SC models may enhance their performance, ensuring that rare diseases are considered sooner and diagnosed more accurately.
The objectives of our study were to incorporate expert interview vignettes into AI-powered SCs, in addition to a traditional literature review, and to evaluate whether this novel approach improves diagnostic accuracy and user satisfaction for rare diseases, focusing on Fabry disease.
This mixed methods prospective pilot study was conducted at Hannover Medical School, Germany. In the first phase, guided interviews were conducted with medical experts specialized in Fabry disease to create clinical vignettes that enriched the AI SC's Fabry disease model. In the second phase, adult patients with a confirmed diagnosis of Fabry disease used both the original and optimized SC versions in a randomized order. The versions, containing either the original or the optimized Fabry disease model, were evaluated based on diagnostic accuracy and user satisfaction, which were assessed through questionnaires.
Three medical experts with extensive experience in lysosomal storage disorder Fabry disease contributed to the creation of 5 clinical vignettes, which were integrated into the AI-powered SC. The study compared the original and optimized SC versions in 6 patients with Fabry disease. The optimized version improved diagnostic accuracy, with Fabry disease identified as the top suggestion in 33% (2/6) of cases, compared to 17% (1/6) with the original model. Additionally, overall user satisfaction was higher for the optimized version, with participants rating it more favorably in terms of symptom coverage and completeness.
This study demonstrates that integrating expert-derived clinical vignettes into AI-powered SCs can improve diagnostic accuracy and user satisfaction, particularly for rare diseases. The optimized SC version, which incorporated these vignettes, showed improved performance in identifying Fabry disease as a top diagnostic suggestion and received higher user satisfaction ratings compared to the original version. To fully realize the potential of this approach, it is crucial to include vignettes representing atypical presentations and to conduct larger-scale studies to validate these findings.
罕见病影响着全球数百万人，是一项重大挑战，因为往往需要数年时间才能做出准确诊断。这种延迟给患者和医疗保健系统带来了沉重负担，因为误诊会导致治疗不足和成本增加。人工智能驱动的症状检查器（SCs）为在诊断过程中更早地发现罕见病提供了机会。然而，这些工具主要基于已发表的文献，而这些文献通常包含关于罕见病的不完整数据，从而影响了诊断准确性。将专家访谈见解整合到SCs模型中可能会提高其性能，确保更快地考虑罕见病并更准确地进行诊断。
我们研究的目的是除了进行传统的文献综述外，还将专家访谈案例纳入人工智能驱动的SCs中，并评估这种新方法是否能提高罕见病的诊断准确性和用户满意度，重点关注法布里病。
这项混合方法前瞻性试点研究在德国汉诺威医学院进行。在第一阶段，对专门研究法布里病的医学专家进行了指导性访谈，以创建丰富人工智能SCs法布里病模型的临床案例。在第二阶段，确诊为法布里病的成年患者以随机顺序使用原始版和优化版SCs。基于诊断准确性和用户满意度对包含原始或优化法布里病模型的版本进行评估，通过问卷调查来评估。
三位在溶酶体贮积症法布里病方面有丰富经验的医学专家参与创建了5个临床案例，并将其整合到人工智能驱动的SCs中。该研究在6例法布里病患者中比较了原始版和优化版SCs。优化版提高了诊断准确性，在33%（2/6）的病例中，法布里病被列为首要建议，而原始模型为17%（1/6）。此外，优化版的总体用户满意度更高，参与者在症状覆盖范围和完整性方面对其评价更高。
本研究表明，将专家提供的临床案例整合到人工智能驱动的SCs中可以提高诊断准确性和用户满意度，尤其是对于罕见病。纳入这些案例的优化版SCs在将法布里病列为首要诊断建议方面表现更优，与原始版相比，获得了更高的用户满意度评分。为了充分发挥这种方法的潜力，纳入代表非典型表现的案例并进行更大规模的研究以验证这些发现至关重要。
