When machines misread science: creating guardrails for human and AI interpretation of biomedical research

Scientific information is increasingly being consumed in AI-summarized form, and the first “reader” and interpreter of a study is commonly becoming a machine (e.g., ChatGPT, OpenEvidence) 1 , 2 , 3 , 4 . Though these technologies offer promising new opportunities, they are also prone to interpretive inaccuracies which can color public perceptions and undermine sound research, treatment decisions, clinical guideline development, public health decision-making, and global health equity 5 , 6 , 7 . A prevailing assumption is that these challenges will be solved primarily by more advanced AI; we contend that science itself can also adapt by developing clearer, more intentional ways of communicating results to the AI systems that increasingly interpret it.