Using Apple's ResearchKit and CareKit Frameworks for Explainable Artificial Intelligence Healthcare

Abstract

Artificial intelligence (AI) has been a topic of
discussion in the healthcare industry for a
long time. The potential of AI to revolutionize
healthcare and medicine is significant.
However, some major obstacles to
implementing AI systems in the healthcare
industry exist. One such obstacle is more
transparency around how these systems work.
If AI systems are not transparent, it becomes
difficult for clinicians and patients to trust
them. To address this issue, Apple's opensource ResearchKit and CareKit frameworks
offer opportunities to develop explainable AI
tools that provide human-readable
explanations alongside recommendations or
predictions. These frameworks allow
developers to create AI systems that provide a
rationale for their outputs, indicate levels of
confidence, and allow for the evaluation of
fairness and bias. This article examines the
capabilities of ResearchKit and CareKit to
create transparent, interpretable AI systems.
Developing trustworthy and easily
understandable AI systems with these
frameworks can bridge the gap between
technologists and clinical end-users. However,
achieving this will require extensive
validation studies and close collaboration
between technologists and clinical end-users.
Some outstanding issues around privacy and
data sharing need to be addressed. By
addressing these issues, we can develop
trustworthy AI systems that clinicians and
patients can easily understand and act upon.
To sum up, by utilizing ResearchKit and
CareKit, we can create reliable AI systems in
the healthcare sector. Although certain
obstacles must be tackled, we can overcome
them by collaborating and guaranteeing that
the AI systems we design are transparent,
interpretable, and trustworthy