  Machine learning (ML), artificial intelligence (AI) and other modern
statistical methods are providing new opportunities to operationalize
previously untapped and rapidly growing sources of data for patient benefit.
Whilst there is a lot of promising research currently being undertaken, the
literature as a whole lacks: transparency; clear reporting to facilitate
replicability; exploration for potential ethical concerns; and, clear
demonstrations of effectiveness. There are many reasons for why these issues
exist, but one of the most important that we provide a preliminary solution for
here is the current lack of ML/AI- specific best practice guidance. Although
there is no consensus on what best practice looks in this field, we believe
that interdisciplinary groups pursuing research and impact projects in the
ML/AI for health domain would benefit from answering a series of questions
based on the important issues that exist when undertaking work of this nature.
Here we present 20 questions that span the entire project life cycle, from
inception, data analysis, and model evaluation, to implementation, as a means
to facilitate project planning and post-hoc (structured) independent
evaluation. By beginning to answer these questions in different settings, we
can start to understand what constitutes a good answer, and we expect that the
resulting discussion will be central to developing an international consensus
framework for transparent, replicable, ethical and effective research in
artificial intelligence (AI-TREE) for health.
