CDTH Seminar: From Diagnosis to Treatment - Augmenting clinical decision making with Artificial Intelligence

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Melbourne Centre for Data Science is pleased to present this seminar in conjuction with the Centre for Digital Transformation of Health.

This seminar is presented by Associate Professor Jenna Wiens, an Associate Professor of Computer Science and Engineering, Associate Director of the AI Lab, and Co-Director of Precision Health at the University of Michigan in Ann Arbor.
Her primary research interests lie at the intersection of machine learning, data mining, and healthcare.
A/Prof Wiens received her PhD from MIT in 2014, was named to the MIT Tech Review’s list of Innovators Under 35 in 2017, and recently was awarded a Sloan Research Fellowship in Computer Science. For more about A/Prof Wiens, visit http://www-personal.umich.edu/~wiensj/

Seminar title: From Diagnosis to Treatment: Augmenting clinical decision making with Artificial Intelligence

When: Thu 2 September, 10am - 11am AEST

Where: This seminar will be delivered via Zoom, please register here - https://events.unimelb.edu.au/event/11644-from-diagnosis-to-treatment-augmenting-clinical

Abstract:

Though the potential of artificial intelligence (AI) in healthcare warrants genuine enthusiasm, meaningful impact will require careful integration into clinical care. AI tools are susceptible to mistakes and rarely capable of capturing all of the nuances pertaining to a complex clinical situation. Thus, we propose approaches designed to augment, rather than replace, clinicians during clinical decision making.

In this talk, Associate Professor Jenna Wiens will highlight three related research directions pertaining to:
i) a transfer learning approach for mitigating potentially harmful shortcuts when making diagnoses
ii) a simple yet accurate deterioration index that generalizes across hospitals and
iii) lessons learned during deployment of a risk stratification tool for predicting healthcare-associated infections.

In summary, there’s a critical need for machine learning in healthcare; however, the safe and meaningful adoption of these techniques will require collaboration between clinicians and AI.