Maxim Khan explores how artificial intelligence can truly serve scientific discovery, with a focus on making AI systems rigorous, transparent and worthy of researchers’ trust. His writing examines the gap between generic AI tools and the demanding standards of scientific work, arguing that models must be built for traceability, reproducibility and careful human scrutiny.

By reading stories under this tag, you’ll gain a clearer view of what it takes to integrate AI into real research workflows without sacrificing quality or reliability. Khan highlights practical risks scientists face when relying on opaque systems, and points toward approaches that keep human judgment at the center while still taking advantage of powerful new tools.