A clinician-scientist shares their journey of learning to code, highlighting the challenges faced before the advent of advanced AI tools. Previously, coding required extensive troubleshooting and patience, but now AI can quickly generate complex analyses and visualizations. Despite this, the scientist argues that learning to code remains essential, though the focus should shift from syntax mastery to effectively guiding AI tools. Early-career scientists now take on roles similar to senior software engineers, overseeing AI-generated outputs. The scientist advises thinking like a systems architect and using modular approaches to maximize AI’s potential without relying on fragile, monolithic scripts.
QUESTION: How might the integration of AI in coding change the skills that future scientists need to develop?
In the AI era, coding means thinking like a systems architect
