From Diagnosis to Treatment: Augmenting Clinical Decision Making with Artificial Intelligence

Join this talk featuring Associate Professor Jenna Wiens on 2 September 2021 at 10.00 AM AEST / 07.00 AM WIB, and discover the research directions pertaining to:

• a transfer learning approach for mitigating potentially harmful shortcuts when making diagnoses,
• a simple yet accurate deterioration index that generalizes across hospitals, and
• lessons learned during deployment of a risk stratification tool for predicting healthcare-associated infections.

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 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.