# From Bit To Bedside: A Practical Framework For Artificial Intelligence Product Development In Healthcare

arXiv: https://arxiv.org/abs/2003.10303 · HTML: https://mlsanity.com/paper/2003.10303/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Artificial Intelligence in Healthcare: Lost In Translation?](https://mlsanity.com/paper/2107.13454/) (2021)
- [The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards](https://mlsanity.com/paper/2101.10790/) (2021)
- [Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare](https://mlsanity.com/paper/2410.00366/) (2024)
- [Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop](https://mlsanity.com/paper/2603.13286/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) in healthcare holds great potential to expand access to high-quality medical care, whilst reducing overall systemic costs. Despite hitting the headlines regularly and many publications of proofs-of-concept, certified products are failing to breakthrough to the clinic. AI in healthcare is a multi-party process with deep knowledge required in multiple individual domains. The lack of understanding of the specific challenges in the domain is, therefore, the major contributor to the failure to deliver on the big promises. Thus, we present a decision perspective framework, for the development of AI-driven biomedical products, from conception to market launch. Our framework highlights the risks, objectives and key results which are typically required to proceed through a three-phase process to the market launch of a validated medical AI product. We focus on issues related to Clinical validation, Regulatory affairs, Data strategy and Algorithmic development. The development process we propose for AI in healthcare software strongly diverges from modern consumer software development processes. We highlight the key time points to guide founders, investors and key stakeholders throughout their relevant part of the process. Our framework should be seen as a template for innovation frameworks, which can be used to coordinate team communications and responsibilities towards a reasonable product development roadmap, thus unlocking the potential of AI in medicine.