# When Performance is not Enough -- A Multidisciplinary View on Clinical Decision Support

arXiv: https://arxiv.org/abs/2204.12810 · HTML: https://mlsanity.com/paper/2204.12810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 27 Apr 2022.

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

- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies](https://mlsanity.com/paper/2007.15911/) (2020)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [Trust and Medical AI: The challenges we face and the expertise needed to overcome them](https://mlsanity.com/paper/2008.07734/) (2020)

## Abstract (arXiv)

Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective. However, beyond such often short-lived improvements, much more needs to be taken into consideration if we want to arrive at a sustainable progress in healthcare. What does it take to actually implement such a system, make it usable for the domain expert, and possibly bring it into practical usage? Targeted at Computer Scientists, this work presents a multidisciplinary view on machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. Along with an implemented risk prediction system in nephrology, challenges and lessons learned in a pilot project are presented.