Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial

Knitza J, Tascilar K, Fuchs F, Mohn J, Kuhn S, Bohr D, Muehlensiepen F, Bergmann C, Labinsky H, Morf H, Araujo E, Englbrecht M, Vorbrüggen W, Decken CBvd, Kleinert S, Ramming A, Distler J, Bartz-Bazzanella P, Vuillerme N, Schett G, Welcker M, Hueber A (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 26

Article Number: e55542

DOI: 10.2196/55542

Abstract

Background: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently. Objective: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)–based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs. Methods: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists. Results: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport’s disease suggestion and Ada’s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada’s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada’s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs. Conclusions: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.

Authors with CRIS profile

Involved external institutions

Universitätsklinikum Gießen und Marburg (UKGM) Germany (DE) RheumaDatenRhePort GbR Germany (DE) University of Grenoble Alpes (UGA) / Université de Grenoble France (FR) Verein zur Förderung der Rheumatologie e.V. Germany (DE)

How to cite

APA:

Knitza, J., Tascilar, K., Fuchs, F., Mohn, J., Kuhn, S., Bohr, D.,... Hueber, A. (2024). Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial. Journal of Medical Internet Research, 26. https://doi.org/10.2196/55542

MLA:

Knitza, Johannes, et al. "Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial." Journal of Medical Internet Research 26 (2024).

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