Titlebook: AI for Health Equity and Fairness; Leveraging AI to Add Arash Shaban-Nejad,Martin Michalowski,Simone Bianc Book 2024 The Editor(s) (if appl

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Arash Shaban-Nejad,Martin Michalowski,Simone BiancHighlights the latest achievements in the use of AI in improving healthy equity.Includes revised versions of selected papers presented at the 2024 AAAI Workshop on Health Intelligence.Interconnects th

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AI for Health Equity and Fairness978-3-031-63592-2Series ISSN 1860-949X Series E-ISSN 1860-9503

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,Navigating the Synthetic Realm: Harnessing Diffusion-Based Models for Laparoscopic Text-to-Image GeA validation study with a human assessment survey underlines the realistic nature of our synthetic data, as medical personnel detects actual images in a pool with generated images causing a false-positive rate of 66%. In addition, the investigation of a state-of-the-art machine learning model to rec

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,Using Large Language Models for Generating Smart Contracts for Health Insurance from Textual Policisess the LLM output, we propose ., ., ., ., and . as metrics. Our evaluation employs three health insurance policies (.) with increasing difficulty from Medicare’s official booklet. Our evaluation uses GPT-3.5 Turbo, GPT-3.5 Turbo 16K, GPT-4, GPT-4 Turbo and CodeLLaMA. Our findings confirm that LLMs

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Can GPT Improve the State of Prior Authorization Via Guideline Based Automated Question Answering?,s introduce our own novel prompting technique. Moreover, we report qualitative assessment by humans on the natural language generation outputs from our approach. Results show that our method achieves superior performance with the mean weighted F1 score of 0.61 as compared to its standard counterpart

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Knowledge-Grounded Medical Dialogue Generation,n effectiveness. First, we build a knowledge bank of recorded patient-provider genetic counseling sessions and leverage an open-source LLM to extract and summarize relevant information. We leverage this knowledge bank to develop a retrieval-augmented system for answering patient questions. We find t