Artificial intelligence hallucinations in large language models for medical education and healthcare: a scoping review of detection, mitigation, and governance
Year: 2026
Type: review
Abstract: BACKGROUND AND OBJECTIVE: Generative artificial intelligence (AI) and large language models (LLMs) are increasingly used in medical education and healthcare. Yet AI hallucinations-plausible but inaccurate, unsupported, or fabricated information or citations-may compromise educational integrity, clinical judgment, patient safety, and trust. This scoping review mapped evidence on their nature, detection and validation, mitigation, and educational and governance implications. METHODS: This scoping review was conducted in accordance with the Joanna Briggs Institute methodology and informed by the Arksey and O'Malley framework. Reporting followed the PRISMA Extension for Scoping Reviews. PubMed/MEDLINE, Scopus, IEEE Xplore, and Web of Science were systematically searched for English-language sources published from January 1, 2018, to July 31, 2026. Study selection and data extraction were independently performed by two reviewers. Findings were synthesized using descriptive thematic analysis. RESULTS: Of 2,051 identified records, 21 sources were included in the final synthesis after duplicate removal and screening. These included five empirical or model-evaluation studies and 16 review, conceptual, or opinion-based sources. The analysis identified five main themes and 13 subthemes: (1) conceptualization and manifestations of AI hallucinations in healthcare; (2) detection, assessment, and validation of hallucinations; (3) mitigation strategies for improving the reliability of generative AI outputs; (4) educational implications and critical AI literacy; and (5) governance, accountability, and trustworthy implementation in healthcare. Retrieval-augmented generation, structured prompt design, output-verification methods, and human oversight were among the most frequently identified approaches for reducing hallucination-related risks. CONCLUSION: AI hallucinations are not merely technical errors; they represent a multidimensional challenge to the safe and responsible use of LLMs in medical education and healthcare. Reducing their risks requires a multilayered approach that includes evidence-based validation, human oversight, critical AI literacy, redesign of educational assessment, and transparent governance and accountability frameworks.
Source: Medical Education Online
Authors: Sara heydari, zohreh sadat alavi
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,189
OpenAlex ID: W7225426342