Seeing,Interpreting,and Trusting Images in the Age of Artificial Intelligence

Artificial intelligence (AI) is transforming how cell biologists generate, analyze, and interpret visual data. Automated pipelines and large-scale image analysis increase throughput and reproducibility, yet they also modify how visual evidence is validated and trusted. This Perspective examines the conceptual implications of this shift, arguing that the integration of AI into imaging demands rather than replaces human morphological expertise. We distinguish between three conceptually distinct phenomena: (1) automation of acquisition, (2) AI-driven interpretation, and (3) the emergence of synthetic images generated without experimental basis. Through examples ranging from classical morphological misinterpretations to the rise of generative models, we show how both human and algorithmic systems can distort meaning when interpretation is detached from context. While AI can match or surpass human precision, metavisual competence remains essential to distinguish signals from artifacts. The current challenge is not technological but scientific: to ensure that computational power and human insight co-evolve toward reliable visual knowledge. We call for a renewed education in critical visual literacy, formal recognition of imaging specialists, and transparent standards of image provenance, such as the publication of raw instrument metadata. Preserving interpretative competence is crucial for the integrity of visual evidence in biomedical science: