# Towards better healthcare: What could and should be automated?

arXiv: https://arxiv.org/abs/1910.09444 · HTML: https://mlsanity.com/paper/1910.09444/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals](https://mlsanity.com/paper/2201.07040/) (2022)
- [From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care](https://mlsanity.com/paper/2506.13584/) (2025)
- [From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion](https://mlsanity.com/paper/2603.26588/) (2026)

## Abstract (arXiv)

While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what should be automated. We quantitatively investigate these research questions by utilizing probabilistic machine learning models trained on thousands of ratings, provided by both healthcare practitioners and automation experts. Based on our findings, we present an analytical tool (Automatability-Desirability Matrix) to support policymakers and organizational leaders in developing practical strategies on how to harness the positive power of automation technologies, while accompanying change and empowering stakeholders in a participatory fashion.