ShareFrom the algorithm development sandbox to the clinical wilderness.
Today,
far too many articles and blog posts suggest that artificial
intelligence (AI) and machine learning (ML) is some sort of magic pill
that can easily be taken to ensure that all and any problems within
healthcare will disappear overnight. However, change is difficult and
often a slow process. It is not surprising to see today’s AI and ML hype
with great hopes and expectations surrounding it, but where actual
implementations and deployments in clinical practice is more of a
dream than a reality. One of the reasons to this is because of the
divisive chasm between the controlled sandbox where algorithm
development happens and the clinical wilderness where healthcare
happens.
Moving from the algorithm development sandbox to the clinical wilderness is associated with several challenges:
1. Providing and demonstrating clinical value
2. Accessing relevant training data
3. Building user-friendly AI/ML applications
4. Deployment and integration with clinical workflows
Providing and demonstrating clinical value
To
develop algorithms that will be used in clinical care, the
developers/researchers need to focus on problems that are of importance
to the end users (the healthcare personnel), the management of the end
users or the customers of the end users (the patients). For example,
will the algorithm make the physicians more efficient or even more
effective, or will it allow the physicians to provide care that was not
earlier possible to provide?
Accessing relevant training data
“Data
is the new oil!” We have all heard this and it is especially true for
ML where access to data is key when training new algorithms. Over the
past decade a lot has happened in terms of open access and making even
medical image data available. For instance, the Cancer Imaging Archive
or Grand Challenges in Biomedical Image Analysis are great sources
for anyone looking for medical image data to train their algorithms.
However, these sources can only get you so far as the data is often
limited in number of samples and sources. Hence, to ensure robustness of
any trained algorithm, it becomes important to establish access to
additional data sources.
Building user-friendly applications
An
ill-designed user interface can render an excel- lent algorithm
useless, whereas a well-designed user interface can turn a mediocre
algorithm into a highly useful tool. Another aspect of this is that
algorithms are not perfect. Hence, user friendly AI applications that
ensure that failed predictions are easily spotted and handled are
essential, especially in healthcare.
Deployment and integration with clinical workflows
Healthcare IT is not what it used to be decades ago. Today’s healthcare IT is a lot more standardised and with significantly more security routines in place, which is good, but which makes it difficult, especially for non-established entities, to deploy their new AI applications. What kind of HW and SW will the application run on? Will the application run on premise or in the cloud? How is access to protected health information handled? Who will have access to this information? These are just some of all the questions that will be asked by the healthcare IT team.Once the questions related to the deployment have been handled and answered satisfactorily, there is still the challenge of integrating your AI application within an existing workflow. For example, switching work- stations to access another application is most of the time out of the question, and even switching applications on the same workstation is frowned upon. both aspects, deployment and integration, will be a lot easier to handle with an IT system in place capable of integrating 3rd party applications through standardised protocols and interfaces.
