  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.
