Intern Emerg Med. 2026 May 17. doi: 10.1007/s11739-026-04393-z. Online ahead of print.
ABSTRACT
Large language models (LLMs) represent a rapidly advancing subset of artificial intelligence with significant potential to augment clinical practice. These tools can assist in literature synthesis, documentation, clinical reasoning, and patient communication. Despite demonstrated capabilities, adoption in healthcare remains limited due to barriers such as limited AI literacy, trust concerns, integration challenges, and generational disparities in digital proficiency. This review critically examines the role of LLMs as cognitive adjuncts in medicine, emphasizing the need for calibrated trust, human oversight, structured implementation strategies, and robust regulatory governance. Evidence suggests that stepwise integration of LLMs, beginning with low-risk tasks, may enhance physician efficiency, reduce cognitive burden, and may support high-quality care. Key limitations, including hallucinations, data currency, environmental impact, and data security, have to be addressed, alongside the need for continuous model refinement. Rigorous clinical trials are needed to establish the efficacy of LLMs. This includes the recognition that LLMs trained on vast web data may contain personal health information, creating a significant privacy risk. Responsible adoption of LLMs is essential to meet the demands of modern medicine while preserving clinical accountability and patient-centered care.
PMID:42143662 | DOI:10.1007/s11739-026-04393-z