# Self-Diagnosis through AI-enabled Chatbot-based Symptom Checkers: User Experiences and Design Considerations

arXiv: https://arxiv.org/abs/2101.04796 · HTML: https://mlsanity.com/paper/2101.04796/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2021.

## Built on by (most cited first, 4 of 4)

- [MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score](https://mlsanity.com/paper/2210.05875/) (2022)
- [Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction](https://mlsanity.com/paper/2408.05555/) (2024)
- [Detecting Autism Spectrum Disorders with Machine Learning Models Using Speech Transcripts](https://mlsanity.com/paper/2110.03281/) (2021)
- [Taxonomy of Comprehensive Safety for Clinical Agents](https://mlsanity.com/paper/2509.22041/) (2025)

## Abstract (arXiv)

Recently, there has been a growing interest in developing AI-enabled chatbot-based symptom checker (CSC) apps in the healthcare market. CSC apps provide potential diagnoses for users and assist them with self-triaging based on Artificial Intelligence (AI) techniques using human-like conversations. Despite the popularity of such CSC apps, little research has been done to investigate their functionalities and user experiences. To do so, we conducted a feature review, a user review analysis, and an interview study. We found that the existing CSC apps lack the functions to support the whole diagnostic process of an offline medical visit. We also found that users perceive the current CSC apps to lack support for a comprehensive medical history, flexible symptom input, comprehensible questions, and diverse diseases and user groups. Based on these results, we derived implications for the future features and conversational design of CSC apps.