Another leading pioneer in bridging academia and clinical practice is Mihaela van der Schaar, professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge. Her research lab develops cutting-edge machine learning and AI methods for healthcare in close collaboration with clinicians and patients.
“The complexity of healthcare requires that we address numerous and diverse challenges: from improving the quality of healthcare data to developing new methods to help personalize care, as well as making the outcomes of those models interpretable and trustworthy for clinicians and patients. Machine learning can help address all those challenges, and that’s exactly what our lab is focusing on.”
For example, to address privacy-related challenges in data access for AI development, Van der Schaar and her team have been pioneering the use of generative models to create so-called synthetic data. “Synthetic data aims to reproduce the statistical properties of a real-world dataset while safeguarding patient privacy,” she explains. “As an additional benefit, synthetic data can also increase the fairness of AI models by correcting for potential biases in real-world data.”
Van der Schaar and her lab are also pushing the boundaries in using machine learning to solve real-world clinical challenges, such as personalizing treatment plans for patients with complex conditions like cancer. “Different cancer patients have different disease trajectories and respond differently to the same treatment. Using advanced machine learning models, we can make care more personalized by helping physicians decide what treatment may work best for a particular patient at a specific moment of time, given their unique characteristics and history.”
Such models will only gain clinical adoption if the outcomes are highly accurate as well as interpretable and trustworthy, Van der Schaar adds. Through regular engagement sessions with clinicians, she has learned that they have much higher expectations of the interpretability of AI models than is commonly assumed.
“Clinicians want more than an explanation of how certain patient features underpin a model’s prediction. They want the same level of transparency as from classic statistical methods such as regression analysis, and understand what rules or laws the model has unraveled. To meet these needs, we are now working on symbolic metamodels that can help demystify the black box of AI for clinicians.”
Having that ongoing dialogue with clinicians – and patients – is crucial for successful AI innovation in healthcare, says Van der Schaar. “Ultimately, AI should augment human skills and empower both healthcare professionals and patients, creating a true human-machine partnership. We can only achieve that goal by working closely together.”