Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers
Open accessAbstract
Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs.
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Several mechanisms have been proposed to optimize the combination of human and AI decisions and facilitate appropriate reliance, including interpretable explanations, cognitive forcing functions, and feedback loops.
“These mechanisms include providing interpretable explanations for AI advice [21, 72, 123], using cognitive forcing functions[17, 47, 99], and incorporating feedback loops to enhance the interaction between humans and AI systems [10, 11, 139].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [123]
HCI studies on XAI have found somewhat mixed results on enhancing user trust, satisfaction, and willingness to adopt AI systems.
“HCI studies on XAI have found explanations to improve user understanding of AI systems [12,22,32], and somewhat mixed results on enhancing user trust [22], satisfaction [32] and willingness to adopt AI systems [80].”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [80]
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“[50] Perceived Transparency I The song recommendations made sense to me.”
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- Authors
- Tsai, C.-H., You, Y., Gui, X., Kou, Y., & Carroll, J. M.
- Affiliations
- Pennsylvania State University (US)
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-17
- OpenAlex
- W3163128657
- Cited by
- 86
- References
- 74
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 17 · 2023: 17 · 2022: 20 · 2021: 4
- FWCI
- 6.70
- Type
- conference paper
- Language
- en
- Open access
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- DigitalCommons@UNO (submittedVersion)
- Funders
- Penn State College of IST's seed grant (150000004308 INTR)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- online symptom checkers, explainable AI, self-diagnosis, triage, COVID-19 pandemic, user experience, conversation flow, remote care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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