[D]How to understand real problems + data in climate/health AI before choosing a lane?

I’m a data scientist with experience in demand forecasting (operations / supply chain). I’m starting a more advanced deep learning class and I’m hoping to pivot toward more frontier-oriented work other fields: climate/environment, multimodal ML, and human health (wearables/digital biomarkers, biotech, clinical AI), or more later.

Right now I’m missing the domain context: I don’t have a good mental map of what the real problems are in these areas today, what the data and constraints look like, and where AI genuinely helps. I’d love to learn enough to gauge my interest and pick a lane to go deep.

What books or reports would you recommend to understand the problem landscape in these sectors?