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Your search keyword '"Ranganath P"' showing total 480 results
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480 results on '"Ranganath P"'

1. ChatGPT versus a Customized AI Chatbot (Anatbuddy) for Anatomy Education: A Comparative Pilot Study

Author

Gautham Arun, Vivek Perumal, Francis Paul John Bato Urias, Yan En Ler, Bryan Wen Tao Tan, Ranganath Vallabhajosyula, Emmanuel Tan, Olivia Ng, Kian Bee Ng, and Sreenivasulu Reddy Mogali

Abstract

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes.

Published
2024
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3. Predicting hematoma expansion after intracerebral hemorrhage: a comparison of clinician prediction with deep learning radiomics models

Author

Yu, Boyang, Melmed, Kara R., Frontera, Jennifer, Zhu, Weicheng, Huang, Haoxu, Qureshi, Adnan I., Maggard, Abigail, Steinhof, Michael, Kuohn, Lindsey, Kumar, Arooshi, Berson, Elisa R., Tran, Anh T., Payabvash, Seyedmehdi, Ironside, Natasha, Brush, Benjamin, Dehkharghani, Seena, Razavian, Narges, and Ranganath, Rajesh

Published
2025
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27. From algorithms to action: improving patient care requires causality

Author

van Amsterdam, Wouter A. C., de Jong, Pim A., Verhoeff, Joost J. C., Leiner, Tim, and Ranganath, Rajesh

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society, Statistics - Machine Learning
Abstract

In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.

Published
2022
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40. Population Predictive Checks

Author

Moran, Gemma E., Blei, David M., and Ranganath, Rajesh

Subjects
Statistics - Methodology, Computer Science - Machine Learning
Abstract

Bayesian modeling helps applied researchers articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers can now easily build, use, and revise complicated Bayesian models for large and rich data. These capabilities, however, bring into focus the problem of model criticism. Researchers need tools to diagnose the fitness of their models, to understand where they fall short, and to guide their revision. In this paper we develop a new method for Bayesian model criticism, the population predictive check (Pop-PC). Pop-PCs are built on posterior predictive checks (PPCs), a seminal method that checks a model by assessing the posterior predictive distribution on the observed data. However, PPCs use the data twice -- both to calculate the posterior predictive and to evaluate it -- which can lead to overconfident assessments of the quality of a model. Pop-PCs, in contrast, compare the posterior predictive distribution to a draw from the population distribution, a heldout dataset. This method blends Bayesian modeling with frequenting assessment. Unlike the PPC, we prove that the Pop-PC is properly calibrated. Empirically, we study Pop-PC on classical regression and a hierarchical model of text data.

Published
2019
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