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TRUVACE RECORD VERSION
record: TRV-2026-0506
version: 4
kind: sources_changed
reason: Source set updated
timestamp: 2026-10-15T22:03:09.971546Z
status: published
lens: g_space
sector: health
headline: Can AI assist in reducing diagnostic error? A narrative review
dek: 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),…
gain_title: AI and large language models have matured to improve clinicians' diagnostic decision-making during bedside and clinic consultations and help institutions increase diagnostic safety, addressing preventable diagnostic errors.
problem_title: (none)
trace_subject: (none)
gain_reading: AI and large language models have matured to improve clinicians' diagnostic decision-making during bedside and clinic consultations and help institutions increase diagnostic safety, addressing preventable diagnostic errors.
gain_evidence: AI tools have matured to the extent that they can improve diagnostic decision-making of clinicians and can assist institutions in increasing diagnostic safety | About 80 % of diagnostic errors are potentially preventable, most resulting from flaws in clinician reasoning in formulating and testing diagnostic hypotheses
problem_reading: (none)
problem_evidence: (none)
quick_read: A July 2026 narrative review in Diagnosis examined whether artificial intelligence and large language models can reduce diagnostic error in bedside and clinic consultations. It summarized evidence that diagnostic errors affect 5-10% of admissions and visits and contribute to patient harm and hospital mortality.

The authors concluded that AI tools have matured enough to improve clinician decision-making and help institutions increase diagnostic safety, while emphasizing that LLMs should complement rather than replace clinician reasoning and that evolving models require continuous monitoring.
limitation: Rapid LLM iteration means diagnostic capabilities are not stable and require ongoing evaluation.
tag: Evidence-backed gain
key_points: Diagnostic error affects 5-10% of hospital admissions and clinic visits and accounts for 10% of hospital deaths and serious adverse events. | About 80% of diagnostic errors are potentially preventable and stem from flaws in clinician reasoning in formulating and testing hypotheses. | Review examined AI and LLMs specifically within clinician-patient encounters at bedside or clinic.
rundown: The review frames diagnostic error as missed, wrong, delayed, or uncommunicated diagnoses, common in hospital and clinic settings, causing harm in up to 1 in 100 encounters. It attributes most preventable errors to clinician reasoning flaws.

It evaluates contemporary state-of-the-art research on AI and LLMs to answer seven clinician-relevant questions about adoption, concluding LLMs should complement rather than replace nuanced clinician reasoning.
sources:
- peer_reviewed | Diagnosis | https://doi.org/10.1515/dx-2026-0021 | 2026-07-22
- peer_reviewed | Diagnostics | https://doi.org/10.3390/diagnostics12102549 | 2022-10-20
- peer_reviewed | npj Artificial Intelligence | https://doi.org/10.1038/s44387-025-00047-1 | 2025-12-01
- peer_reviewed | World Journal of Advanced Research and Reviews | https://doi.org/10.30574/wjarr.2020.8.2.0384 | 2020-11-30
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