# Artificial Intelligence in Healthcare: Lost In Translation?

arXiv: https://arxiv.org/abs/2107.13454 · HTML: https://mlsanity.com/paper/2107.13454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 76 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Reproducibility in Machine Learning for Health](https://mlsanity.com/paper/1907.01463/) (2019)
- [Missing Data Imputation for Supervised Learning](https://mlsanity.com/paper/1610.09075/) (2016)
- [From Bit To Bedside: A Practical Framework For Artificial Intelligence Product Development In Healthcare](https://mlsanity.com/paper/2003.10303/) (2020)
- [Classical Statistics and Statistical Learning in Imaging Neuroscience](https://mlsanity.com/paper/1603.01857/) (2016)
- [Opportunities for artificial intelligence in advancing precision medicine](https://mlsanity.com/paper/1911.07125/) (2019)
- [OnRAMP for Regulating AI in Medical Products](https://mlsanity.com/paper/2010.07038/) (2020)

## Built on by (most cited first, 2 of 2)

- [From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems](https://mlsanity.com/paper/2608.06112/) (2026)
- [Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing](https://mlsanity.com/paper/2607.27428/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) in healthcare is a potentially revolutionary tool to achieve improved healthcare outcomes while reducing overall health costs. While many exploratory results hit the headlines in recent years there are only few certified and even fewer clinically validated products available in the clinical setting. This is a clear indication of failing translation due to shortcomings of the current approach to AI in healthcare. In this work, we highlight the major areas, where we observe current challenges for translation in AI in healthcare, namely precision medicine, reproducible science, data issues and algorithms, causality, and product development. For each field, we outline possible solutions for these challenges. Our work will lead to improved translation of AI in healthcare products into the clinical setting