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Aghiles Hamroun

Publications and source records attributed to Aghiles Hamroun.

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Hallucination by proxy in LLM-assisted differential diagnosis

Current evidence suggests that LLM assistance could augment the diagnostic accuracy of clinicians. However, these systems are black boxes, susceptible to hallucinations, and project a potentially misleading level of confidence. It is currently unknown whether physicians are susceptible to accepting fabricated LLM suggestions, and whether this susceptibility varies with experience. We poisoned the system prompt of an LLM-based diagnostic assistant, forcing it to suggest a fictitious disease (neurocadmiumatosis) within an otherwise legitimate differential diagnosis. Across two independent phases, 18 of 41 participants (44%) incorporated neurocadmiumatosis into their final differential following LLM interaction: 18 of 26 participants with 6 months or less of neuroradiology training (69%) and 0 of 15 participants with >6 months of neuroradiology training (0%). Our results indicate that radiologists, particularly early in their training, are susceptible to LLM hallucinations. This "hallucination by proxy" phenomenon was exclusive to physicians with limited subspecialty experience, underscoring the need for structured training in critical appraisal of AI-generated content.

cs.HC

PARROT: An Open Multilingual Radiology Reports Dataset

Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiology reports spanning multiple languages for testing natural language processing applications in radiology. Materials and Methods: From May to September 2024, radiologists were invited to contribute fictional radiology reports following their standard reporting practices. Contributors provided at least 20 reports with associated metadata including anatomical region, imaging modality, clinical context, and for non-English reports, English translations. All reports were assigned ICD-10 codes. A human vs. AI report differentiation study was conducted with 154 participants (radiologists, healthcare professionals, and non-healthcare professionals) assessing whether reports were human-authored or AI-generated. Results: The dataset comprises 2,658 radiology reports from 76 authors across 21 countries and 13 languages. Reports cover multiple imaging modalities (CT: 36.1%, MRI: 22.8%, radiography: 19.0%, ultrasound: 16.8%) and anatomical regions, with chest (19.9%), abdomen (18.6%), head (17.3%), and pelvis (14.1%) being most prevalent. In the differentiation study, participants achieved 53.9% accuracy (95% CI: 50.7%-57.1%) in distinguishing between human and AI-generated reports, with radiologists performing significantly better (56.9%, 95% CI: 53.3%-60.6%, p<0.05) than other groups. Conclusion: PARROT represents the largest open multilingual radiology report dataset, enabling development and validation of natural language processing applications across linguistic, geographic, and clinical boundaries without privacy constraints.

cs.CL