Accepted for/Published in: JMIR Biomedical Engineering
Date Submitted: Sep 20, 2024
Open Peer Review Period: Oct 8, 2024 - Dec 3, 2024
Date Accepted: Oct 5, 2025
(closed for review but you can still tweet)
From Challenges to Solutions in Applying Large Language Models to Healthcare: Original Research on Guideline-Based Management Planning and Automated Medical Coding
ABSTRACT
Background:
One in ten patients die because of a diagnostic error while doctors spend two hours with paperwork for every hour they spend with patients. Medical knowledge base doubles every 73 days and doctors struggling to keep up experience burnout at peak rates. Successful generative AI applications include data analytics, document generation, and question answering on medical databases, however, generative AI is yet to make its way into hospitals.
Objective:
We aim to accelerate this transition by offering Rhazes, an AI-Assistant for doctors to assist them with paperwork and analytical tasks in medicine.
Methods:
Storage, hosting, AI services and LLMs are provided by Microsoft Azure and OpenAI. Retrieval augmented generation is used to for diagnosis and billing and WebRTC protocol is used for telemedicine.
Results:
We made Rhazes available on the web free of charge and it is ready to help physicians with notetaking, clinical decisions and patient management starting from the initial consultation all the way to patient discharge.
Conclusions:
In the future we aim to pilot Rhazes in hospitals and conduct a thorough evaluation of its performance.
Citation
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Copyright
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