Hallucinations in large language models: cognitive paradox, innovative value and rational examination

Hallucinations in large language models have long been regarded as generative defects inconsistent with facts, especially in clinical scenarios such as ophthalmology, where their risks have been widely emphasized while their underlying cognitive and innovative significance has been largely ignored. Based on the history of science and cognitive logic, this paper proposes a core viewpoint from multiple perspectives including artificial intelligence (AI) mechanism, logic and medical-engineering integration: hallucination is neither correct nor wrong, but an exploratory generation of intelligent systems at the boundary of existing knowledge, with obvious uncertainty and relativity. Constrained by the limitations of staged cognitive levels, humans often label innovative content beyond current consensus as hallucinations. In the history of science, numerous breakthroughs originated from ” cognitive deviations” that were initially regarded as paradoxes or heterodoxies. Continuous model iteration does not eliminate hallucinations but transforms them from random biases into controllable hypothesis-like generation. Under the future human-machine collaboration paradigm, machines are responsible for breakthrough association while humans undertake empirical verification and validation, and their complementarity can systematically unlock the innovative value of hallucinations. On the premise of strict scientific rigor, this paper distinguishes the dual attributes of hallucinations, emphasizes their coexistence of risks and value and the non-absolute nature of right-wrong judgment, and demonstrates their potential positive significance in cognitive expansion and innovative inspiration, so as to provide theoretical support for constructing a more rational and forward-looking framework for trustworthy AI applications.