Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{198514,
        author = {Saima Ansari and Isha Philip and Saniya Saba Sheikh and Sheikh Afreen Mujib and Sheikh Madhiha Mahak and Sumit Mahato},
        title = {CareAssist: Accurate Care Through Intelligent Retrieval},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10200-10207},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198514},
        abstract = {Right now, hospitals struggle under more patients, too few workers, and clunky paperwork routines. By 2034, the U.S. might miss as many as 124,000 doctors - waiting periods already stretch past three weeks on average. Meanwhile, close to eight out of ten medical staff feel worn down, especially from doing the same small jobs over and again, like filling forms or logging details. Though some hope new tools will help, the pressure keeps building where it matters most: time with people.
Facing tough issues in healthcare communication, here comes CareAssist - a smart helper built on GenAI fused with RAG tech. This tool talks like a person, understands questions about health, books visits, shares correct info using everyday language. Instead of guessing, it pulls facts straight from trusted hospital records and medical sources. Because it checks real data before replying, wrong answers happen far less often. Not your average bot, it thinks first by searching, then speaks only when sure.
Tasks like pulling summaries from medical records get handled automatically, freeing up time for hospital teams. In tests it gave correct answers nearly every time - hitting 96 percent right - answered in less than half a second, saving workers more than 130 hours each year. This setup runs smoothly at larger scales while making daily operations faster and care interactions better for patients.},
        keywords = {Biomedical Natural Language Processing (NLP), Intelligent Clinical Information Systems, Generative Artificial Intelligence (GenAI), Healthcare Conversational Agents, and Retrieval-Augmented Generation (RAG).},
        month = {April},
        }