[Andrew]

Pro tip: Use the community’s mentorship program. As a mentee, I’ve gained invaluable insights from experienced members in the field.

[Andrew]

We’ve found that phased rollouts with plenty of feedback loops work well. It allows us to address issues early and build confidence in the AI systems gradually.

• This reply was modified 4 months, 4 weeks ago by  Andrew.

[Andrew]

We’ve had success with implementing a data lake architecture. It allows us to store structured and unstructured data from various sources in a centralized repository. The challenge has been ensuring proper data governance and quality control.

[Andrew]

We’ve had success with micro-learning modules – short, focused lessons that professionals can complete in their spare time. Maybe we could develop a standardized series of these?

[Andrew]

From a commercial standpoint, we’re looking at models where patients can be compensated for the use of their data in AI development. But how do we value data fairly?

[Andrew]

Arthur, this is a complex issue. We’ve been working on multi-objective optimization algorithms that balance efficiency with equity. The challenge is defining ‘fairness’ in a way that’s universally acceptable. 

• This reply was modified 5 months ago by  Andrew.

[Andrew]

We’ve actually been working on a patient-facing app that explains how AI is used in their care plan. Early feedback has been positive, with patients reporting feeling more informed and engaged. Maria, I’d be happy to share some insights from our development process if you’re interested in creating something similar.

[Andrew]

Hi Maria, great topic! As an entrepreneur, we’ve found that early and continuous engagement with end-users is crucial. In our last hospital implementation, we set up an ‘AI Champions’ program, where we trained a select group of clinicians to be internal advocates and trainers. This peer-to-peer approach significantly improved adoption rates. Have you tried something similar?

[Andrew]

From a startup perspective, we’re developing triage AI that incorporates social vulnerability indices. But we’re struggling with how to weight these factors without introducing bias.