We want to design trustworthy AI models for parasite diagnostics that (i) estimate and minimise prediction uncertainty by out-of-distribution generalisation, (ii) exploit the potential of self-supervised learning, (iii) embed
context to come up with general-purpose models invariant to region, patient profile, and parasite type, and (iv) only have minimal computational requirements, thus facilitating deployment on edge devices.