# Responsible AI in Healthcare

arXiv: https://arxiv.org/abs/2203.03616 · HTML: https://mlsanity.com/paper/2203.03616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [Towards Automatic Detection of Misinformation in Online Medical Videos](https://mlsanity.com/paper/1909.01543/) (2019)
- [A giant with feet of clay: on the validity of the data that feed machine learning in medicine](https://mlsanity.com/paper/1706.06838/) (2017)

## Built on by (most cited first, 1 of 1)

- [Fake News Detection and Behavioral Analysis: Case of COVID-19](https://mlsanity.com/paper/2305.16057/) (2023)

## Abstract (arXiv)

This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare. In particular, we illustrate two main research themes related to the work of two laboratories within the Department of Informatics, Systems, and Communication at the University of Milano-Bicocca. The problems addressed concern, in particular, {uncertainty in medical data and machine advice}, and the problem of online health information disorder.