AI-enabled Consumer-facing Health Technology

Oct 5, 2021

Overview

This project aims to understand the socio-technical implications of AI-enabled consumer-facing health technology. We have three papers published and one paper under preparation.

Paper 1

The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology | Paper

Yue You, Yubo Kou, Xianghua Ding, Xinning Gui

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2021 (CHI 2021)

Research question:

How users perceive and understand medical authority with the emergence of consumer-facing AI health technologies

Method:

Semi-structured interviews

Presentation_chi 2021 copy.pptx

Paper 2

Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers | Paper

Chun-Hua Tsai, Yue You, Xinning Gui, Yubo Kou, John M. Carroll

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2021 (CHI 2021)

Research question:

How explanations could be used to promote diagnostic transparency of online symptom checkers

Methods: A mixed study

(1) Semi-structured interviews: to specify user needs for explanations from users of existing symptom checkers


(2)
A lab-controlled experiment: designed a prototype with different explanation styles to explore what kind of and whether explanation can improve user experience

CHI21 copy

Paper 3

Self-Diagnosis through Chatbot-based Symptom Checkers: User Experiences and Design Considerations | Paper

Yue You, Xinning Gui

Proceedings of American Medical Informatics Association 2020 Annual Symposium

(AMIA 2020)

Research question:

To understand how users perceive the effectiveness of chatbot-based symptom checker applications

Methods:

(1) App feature analysis: to shed light on the feature design of the chatbot-based symptom checker apps


(2)
App review analysis and semi-structured interviews: to acquire in-depth insights as to how users perceive chatbot-based symptom checker apps

AMIA Conference Author-Presenter PowerPoint Template_AMIA 2020

Paper 4

This paper aims to understand how users perceive the efficiency and human-like features of symptom checkers.

Methods: A mixed study

(1) Semi-structured interviews
(2)
A lab-controlled experiment