Can AI assist in reducing diagnostic error? A narrative review

Diagnostic error, defined as missed, wrong, or delayed diagnoses or those not communicated to patients, is common, affecting 5-10 % of hospital admissions and clinic visits. Such errors cause patient harm in up to 1 in 100 of such encounters and account for 10 % of all hospital deaths and serious adverse events. About 80 % of diagnostic errors are potentially preventable, most resulting from flaws in clinician reasoning in formulating and testing diagnostic hypotheses. The advent of artificial intelligence (AI), and large language models (LLMs) in particular, has attracted great interest in how these technologies can reduce diagnostic error within the context of bedside or clinic consultations. This narrative review aims to provide practising clinicians with a comprehensible analysis of where AI and LLMs are currently positioned in assisting diagnostic performance in clinician-patient encounters based on contemporary state-of-the-art research. It attempts to answer seven questions relevant to clinician understanding and adoption of AI/LLMs. It concludes that AI tools have matured to the extent that they can improve diagnostic decision-making of clinicians and can assist institutions in increasing diagnostic safety. The rapid development of LLMs and ongoing release of new versions necessitate continuous monitoring of their evolving diagnostic capabilities. Importantly, a balanced approach is required where LLMs work to complement, rather than replace, the nuanced diagnostic reasoning of clinicians.