Dissociation of decision-making from escalated drug intake within a long-access model.

Artificial intelligence (AI), particularly large language models (LLMs), has rapidly evolved from experimental tools into emerging clinical decision‐support systems. These models can synthesise data, streamline documentation and generate structured recommendations. Earlier models such as GPT‐4 showed strong reasoning capability across a range of medical tasks, including diagnostic support, clinical communication, documentation, antimicrobial prescribing and medical knowledge benchmark exams., , , , , Recent studies suggest that newer LLMs achieve higher accuracy than prior models in multimodal medical reasoning and can generate reliable diagnostic support for chronic disease management, including diabetes. While these developments highlight the growing use of LLMs in clinical contexts, safe, institution‐specific implementation remains essential. The transition from potential to structured use has already begun, as evidenced by the successful embedding of GPT‐4 into medical education curricula. The next logical step is a careful, evidence‐based assessment of safety and governance requirements prior to any consideration of clinical deployment. Even infrequent but plausible antimicrobial prescribing errors generated by such systems could propagate rapidly and at scale, with disproportionate consequences for patient safety and antimicrobial resistance, particularly if deployed without formal governance or validation.