Right for the Wrong Reasons: A Benchmark for Hallucination and Clinical Safety in AI Health Triage

Retrieve-Then-Verify for Evaluating Evidence Support and Hallucination in Large Language Model-Generated Medical Information: Empirical Study.

Retrieval-based evidence verification provides a reproducible and transparent approach for evaluating the reliability of AI-generated medical information, with direct relevance to digital health practice, evidence-based medicine, and medical informatics.

Factual Hallucination in Medical Large Language Models: Typology, Evaluation, and Systematic Governance

Large language models (LLMs) have demonstrated transformative potential in clinical documentation generation, diagnostic assistance, and patient consultation. However, their tendency toward “hallucination”— generating semantically fluent but factually inconsistent content with established medical knowledge or input con...

AI Hallucination And Fabricated References: A Growing Crisis For Medical Researchers A Narrative Review

Large language models (LLMs) have introduced a new research integrity threat into the biomedical literature i.e. fabricated references that appear authentic but correspond to no existing publication. This narrative review distinguishes fabrication, the invention of an entirely non-existent source, from unfaithfulness,...

Advancing Generative Artificial Intelligence for Clinical Assessment: Commentary on Chen, Horga, and Escola 2026.

Research protocol: Evaluating Chain-of-Thought Reasoning in LLMs for Complex Clinical Psychiatric Cases

Background Psychiatric medicine presents unique diagnostic and therapeutic challenges, often involving multimorbidity, polypharmacy, and atypical presentations requiring complex reasoning. Artificial Intelligence, particularly Large Language Models (LLMs), is emerging as a support tool in these settings. However, the c...

The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions

As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about scarce medical resources often hinge on judgments of responsibility, particularly when patients'own actions contribute to illness. We investigate how LLMs reason about res...