Published in MetaArXiv, 2026
A systems-level survey arguing that hallucination in medical LLMs is a structural property of probabilistic generation, not a fixable bug. Synthesizes 50+ papers on detection, mitigation, and benchmarking through a risk management lens.
Recommended citation: Matthews, A., Vankadaru, V., Roosta, T., & Passban, P. (2026). Rethinking Medical LLM Hallucinations: A System-Level Survey. MetaArXiv.
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Published in Target: ICML 2026 (Poster), 2026
RAG framework for pediatric medical QA using dual-retrieval architecture combining dense encoders and sparse retrieval (BM25) with age-specific classification. Achieves 34% accuracy improvement and 42% hallucination reduction over baselines.
Recommended citation: Vankadaru, V. et al. (2026). PedRAG: Retrieval-Augmented Generation for Pediatric Medical QA. Target: ICML 2026.
Published in Target: NeurIPS 2026, 2026
First multimodal MIL framework for depression detection on DAIC-WOZ. Combines MT5/RoBERTa text with Wav2Vec 2.0 audio via CTC temporal alignment. Achieves F1>0.90, surpassing prior SOTA. Directly addresses interviewer bias via strict prompt exclusion.
Recommended citation: Vankadaru, V. et al. (2026). Multimodal Multi-Instance Learning for Depression Detection. Target: NeurIPS 2026.