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However, as more consumer-facing health applications are available in the market, this issue can lead the users to make medical decision based on incorrect results. Bhatt et al. bhatt2020uncertainty proposed to leverage uncertainty for users making decision and placing trust in machine learning models.
Various machine learning-based mobile health applications have been proposed to detect health conditions (e.g., traumatic brain injury mariakakis2017pupilscreen , pancreatic cancer mariakakis2017biliscreen , jaundice de2014bilicam ) and vital signals (e.g., heart rate liu2020multi , respiratory rate liu2020multi , heart rate variability huynh2019vitamon , blood pressure schoettker2020blood , SpO2 hoffman2021smartphone ).
Machine learning models that learn from large-scale medical datasets are able to detect various symptoms and conditions, including mental health gkotsis2017characterisation ; su2020deep , retinal disease de2018clinically , lung cancer ardila2019end . AI-powered diagnostic tools ardila2019end ; de2018clinically are intended to assist medical personnel by making unbiased decision based on thousands of examples.
Trustworthy AI Machine learning systems are deployed in real-world settings to billions of users, making significant impacts on high-stake decision making such as healthcare, policy, economy, and transportation.

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