Medical AI is well-suited to complex input, personalised output and scaling through automation
AI is useful in situations where the input is highly complex, where it is desirable for the question to have a complex answer or where the hypothesis space cannot easily be constrained. An example of a highly complex input is imaging data, which may contain numerous features that can represent a large number of pathologies.
AI is good at providing outputs that are personalised to individuals and patient sub-groups, rather than population-level scoring systems that provide generalisable but imprecise predictions for the non-existent ‘average’ patient. (Example: Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence)
AI can expand the scope of research by enabling automation of routine tasks. For example, a research group wanting to anotate a large dataset of computer tomography (CT) would require substantial time and cost with AI. But with AI a subset could be labelled, used to train a classifer and create labels for the remaining images. The same is also true for other data types, such as AI extraction of information from clinical letters using natural language processing.
- Key considerations for the use of AI in healthcare and clinical research by Lovejoy et al.