Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit. Machine learning (ML), artificial intelligence (AI), and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. The potential uses include improving diagnostic accuracy,1 more reliably predicting prognosis,2 targeting treatments,3 and increasing the operational efficiency of health systems.4 Examples of potentially disruptive technology with early promise include image based diagnostic applications of ML/AI, which have shown the most early clinical promise (eg, deep learning based algorithms improving accuracy in diagnosing retinal pathology compared with that of specialist physicians5), or natural language processing used as a tool to extract information from structured and unstructured (that is, free) text embedded in electronic health records.2 Although we are only just …

Machine learning (ML) and artificial intelligence (AI) are becoming very useful technologies in cybersecurity. However, before you can model, validate, and visualize security data that will actually be useful, you need to prepare the data properly for input. This can be a difficult and complicated process – something data scientists wrestle with often. More than just traditional data preparation, which includes cleansing the data and de-duping, ML algorithms often require numerical rather than standard text input. The challenge is finding an efficient and accurate way to convert your data to numerical values that can be consumed by the ML model or algorithm.

"Bias" is an overloaded term which means remarkably different things in different contexts. Here are just a few definitions of bias for your perusal. There are quite a few meanings here, and some of them are spicier than others. The young discipline of ML/AI has a habit of borrowing jargon from every-which-where (sometimes seemingly without looking up the original meaning), so when people talk about bias in AI, they might be referring to any one of several definitions above. Imagine getting yourself prepared for the emotional catharsis of an ornate paper promising to fix bias in AI… only to discover (several pages in) that the bias they're talking about is the statistical one.

"Bias" is an overloaded term which means remarkably different things in different contexts. Here are just a few definitions of bias for your perusal. There are quite a few meanings here, and some of them are spicier than others. The young discipline of ML/AI has a habit of borrowing jargon from every-which-where (sometimes seemingly without looking up the original meaning), so when people talk about bias in AI, they might be referring to any one of several definitions above. Imagine getting yourself prepared for the emotional catharsis of an ornate paper promising to fix bias in AI… only to discover (several pages in) that the bias they're talking about is the statistical one.

"Bias" is an overloaded term which means remarkably different things in different contexts. Here are just a few definitions of bias for your perusal. There are quite a few meanings here, and some of them are spicier than others. The young discipline of ML/AI has a habit of borrowing jargon from every-which-where (sometimes seemingly without looking up the original meaning), so when people talk about bias in AI, they might be referring to any one of several definitions above. Imagine getting yourself prepared for the emotional catharsis of an ornate paper promising to fix bias in AI… only to discover (several pages in) that the bias they're talking about is the statistical one.

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.