5 papers
Planner-Auditor Twin: Agentic Discharge Planning with FHIR-Based LLM Planning, Guideline Recall, Optional Caching and Self-Improvement
Kaiyuan Wu, Aditya Nagori, Rishikesan Kamaleswaran
Objective: Large language models (LLMs) show promise for clinical discharge planning, but their use is constrained by hallucination, omissions, and miscalibrated confidence. We int…
Performance of Large Language Models in Answering Critical Care Medicine Questions
Mahmoud Alwakeel, Aditya Nagori, An-Kwok Ian Wong +3
Large Language Models have been tested on medical student-level questions, but their performance in specialized fields like Critical Care Medicine (CCM) is less explored. This stud…
Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
Aditya Nagori, Ricardo Accorsi Casonatto, Ayush Gautam +2
The surge in scientific publications challenges traditional review methods, demanding tools that integrate structured metadata with full-text analysis. Hybrid Retrieval Augmented G…
Evaluating LLMs in Medicine: A Call for Rigor, Transparency
Mahmoud Alwakeel, Aditya Nagori, Vijay Krishnamoorthy +1
Objectives: To evaluate the current limitations of large language models (LLMs) in medical question answering, focusing on the quality of datasets used for their evaluation. Materi…
Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models
Aditya Nagori, Ayush Gautam, Matthew O. Wiens +6
Clustering patient subgroups is essential for personalized care and efficient resource use. Traditional clustering methods struggle with high-dimensional, heterogeneous healthcare…