ArticleInternational journal of medical informatics2022
Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study.
Article in International journal of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Leveraging chatbots for enhanced decision-making: a comprehensive literature review.Frontiers in artificial intelligence · 2026Pooled it
- The Role of Language in Remote Healthcare Triage: A Meta-Aggregative Review.Journal of advanced nursing · 2025Pooled it
- [Artificial intelligence in spinal surgery: current applications and future perspectives].Orthopadie (Heidelberg, Germany) · 2026Review
- Evaluating the use of red flags by online symptom checkers.BMC health services research · 2025Article
- Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review.Journal of medical Internet research · 2024Review
- The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.JMIR formative research · 2024Article
- Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence.Current urology reports · 2024Review
- ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews.
objectivesThe goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers.
methodsWe evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms.
resultsThe overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73).
conclusionThe data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.