In the context of collaborative AI research and development projects, it would be ideal to have self-contained encapsulated algorithms that can be easily shared between different parties, executed and validated on data at different sites, or trained in a federated manner. In practice, all of this is possible but greatly complicated, because human supervision and expert knowledge is needed to set up the execution of algorithms based on their documentation, possibly implicit assumptions, and knowledge about the execution environment and data involved. We derive and formulate a range of detailed requirements from the above goal and from specific use cases, focusing on medical imaging AI algorithms. Furthermore, we refer to a number of existing APIs and implementations and review which aspects each of them addresses, which problems are still open, and which public standards and ontologies may be relevant. Our contribution is a comprehensive collection of aspects that have not yet been addressed in their entirety by any single solution. Working towards the formulated goals should lead to more sustainable algorithm ecosystems and relates to the FAIR principles for research data, where this paper focuses on interoperability and (re)usability of medical imaging AI algorithms.