How can junior clinicians prepare for the age of AI in medicine?
For junior clinicians at the start of their career the future is both bright and daunting. From taking the Hippocratic oath, the next generation of doctors will be swearing to use whichever technologies arm them with the greatest abilities to help their patients: blockchain, artificial intelligence, augmented and virtual reality, and a wave of fourth industrial revolution technologies are set to change medicine forever. We’ve spoken to junior, forward-thinking clinicians who have told us how they are preparing for this transformation.
Martin Seneviratne – Masters in Biomedical Informatics at Stanford
Junior clinicians need to become experts at framing the question for AI to answer. Algorithms are increasingly becoming the commodity – the challenge is finding the right question and the right dataset to target our GPUs toward. The biggest trap in health AI is answering a complex question that does not change anything for the patient.
Clinicians need to be the ‘sherpas’ helping to find applications for AI where the outcome is clinically actionable, and where the health economics stack up. The other major bottleneck in health AI is the shortage of labelled training data. Clinicians of the future must play an active role in collecting and curating the datasets we need to power the AI revolution, ensuring that patients are protected in the process. If you care about a problem, the best thing you can do is collect good data on it.
Sharib Gaffar – Pediatrics Residency
I believe junior clinicians should develop a thorough understanding of how to navigate mobile operating systems (Android, iOS). A large portion of artificial intelligence development in medicine is currently centered around mobile applications.
There also appears to be a shift away from computers, including laptops, to a predominantly phone-centric user system for the majority of consumer applications. A well-rounded clinician needs to understand not only clinical information, but also the validity and precision of technological information that mobile health apps are now collecting on patients.
Neural networks and other artificial intelligence programs will focus on the same validity and precision of this mobile health information, and a physician who is able to interpret this information stands a clear advantage in creating treatment plans based on provided information.
In addition, clinicians need to ensure that they are comfortable with interpreting statistical information and reaching a conclusion. Since artificial intelligence is still a burgeoning field in medicine, the majority of significant discoveries at this time will be presented in the form of statistical analyses in journal articles. A clinician who is able to understand whether certain technology-derived health data is statistically significant will feel comfortable creating a treatment plan that is most up to date.
