Enhancing Medical NLP Systems: Integrating Upstash Vector and BGE-M3 for Accurate and Ethical Healthcare Data Management with Reduced Bias

Binxi Xie

Keywords: Medical NLP, Upstash Vector, BGE-M3 model, Real-time data retrieval, Semantic understanding, Bias mitigation, Healthcare AI ethics

Abstract: This paper proposes a novel NLP model in healthcare by including Utash Vector for in-time and contextual information retrieval and BGE-M3 for advanced understanding. The model overcomes the challenges posed by the existing systems, such as incomplete data retrieval, a semantically inconsistent database, and algorithm bias. Incorporating bias mitigation measures and fairness audits, it guarantees no unfair treatment of patients belonging to different groups. Aligned with the AMA Code of Medical Ethics, provides proper management of Electronic Health Records in better ways in terms of transparency, confidentiality, and accuracy. Although these problems are relieved, the accuracy of information is still a major issue, the abuse of artificial intelligence remains a risk, and the use of the AMA Code to guide the integration of artificial intelligence has its limitations. Each of these must operate with defensible use of AI and auditing as well as explanation of AI usage in clinical decision-making.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 4956