From diagnosis to treatment: augmenting clinical decision making with Artificial Intelligence
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.