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HealAI: A Healthcare LLM for Effective Medical Documentation

Published: 04 March 2024 Publication History

Abstract

Since the advent of LLM's like GPT4 everyone in various industries has been trying to harness their power. Healthcare is an industry where this is a specifically challenging problem due to the high accuracy requirements. Prompt Engineering is a common technique used to design instructions for model responses, however, its challenges lie in the fact that the generic models may not be trained to accurately execute these specific tasks. We will present our journey of developing a cost-effective medical LLM, surpassing GPT4 in medical note-writing tasks. We'll touch upon our trials with medical prompt engineering, GPT4's limitations, and training an optimized LLM for specific medical tasks. We'll showcase multiple comparisons on model sizes, training data, and pipeline designs that enabled us to outperform GPT4 with smaller models, maintaining precision, reducing biases, preventing hallucinations, and enhancing note-writing style.

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Cited By

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  • (2025)SpecialtyScribe: Enhancing SOAP note Scribing for Medical Specialties using LLMsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3706131(1098-1099)Online publication date: 10-Mar-2025
  • (2025)PRISM-Med: Parameter-Efficient Robust Interdomain Specialty Model for Medical Language TasksIEEE Access10.1109/ACCESS.2024.352504113(4957-4965)Online publication date: 2025
  • (2025)From screens to scenes: A survey of embodied AI in healthcareInformation Fusion10.1016/j.inffus.2025.103033119(103033)Online publication date: Jul-2025
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    cover image ACM Conferences
    WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining
    March 2024
    1246 pages
    ISBN:9798400703713
    DOI:10.1145/3616855
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 04 March 2024

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    Author Tags

    1. domain-specific llm
    2. ehr
    3. finetuning
    4. healthcare
    5. large language models
    6. long context llm
    7. medical domain
    8. medical note writing
    9. pretraining
    10. prompt engineering
    11. retrieval

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    View all
    • (2025)SpecialtyScribe: Enhancing SOAP note Scribing for Medical Specialties using LLMsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3706131(1098-1099)Online publication date: 10-Mar-2025
    • (2025)PRISM-Med: Parameter-Efficient Robust Interdomain Specialty Model for Medical Language TasksIEEE Access10.1109/ACCESS.2024.352504113(4957-4965)Online publication date: 2025
    • (2025)From screens to scenes: A survey of embodied AI in healthcareInformation Fusion10.1016/j.inffus.2025.103033119(103033)Online publication date: Jul-2025
    • (2025)A survey of large language models for healthcare: from data, technology, and applications to accountability and ethicsInformation Fusion10.1016/j.inffus.2025.102963118(102963)Online publication date: Jun-2025
    • (2024)Technology's Role in Fostering Therapist-Client Collaboration and Engagement with GoalsProceedings of the ACM on Human-Computer Interaction10.1145/36870558:CSCW2(1-28)Online publication date: 8-Nov-2024
    • (2024)Harnessing the Power of LLMs: LLM Summarization for Human-Centric DAST Reports2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC60511.2024.00014(33-39)Online publication date: 2-Sep-2024
    • (undefined)TWIN-GPT: Digital Twins for Clinical Trials via Large Language ModelACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3674838

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