International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 6 (November-December 2025) Submit your research before last 3 days of December to publish your research paper in the issue of November-December.

CURAPATH: AI DRIVEN PERSONALIZED TREATMENT PLANS

Author(s) Dr. Pramod R, Ms. Prerana A R, Mr. Subhash H S, Ms. Thanuja C N, Mr. Thoofik Usman A
Country India
Abstract The rising demand for efficient, accessible, and personalized healthcare shows the need for AI-driven solutions that connect patients, doctors, and hospital staff. CuraPath, AI Driven Personalized Treatment Plans, is an intelligent healthcare platform designed to tackle issues like fragmented patient records, delayed diagnoses, and heavy administrative workloads through one integrated system. It uses Natural Language Processing (NLP) and Machine Learning (ML) models powered by GROQ to analyze patient symptoms, make condition predictions, and suggest personalized treatment plans. The platform operates through three secure portals: Patient, Doctor, and Staff. This setup ensures smooth real-time collaboration, quicker clinical decisions, and improved hospital coordination. Patients can easily enter their symptoms and receive AI-based guidance. Doctors get structured insights to aid their diagnoses, and staff can automate routine tasks like scheduling and notifications. By combining AI analytics, predictive insights, and strong data security, CuraPath improves healthcare delivery efficiency, enhances the patient experience, and lightens operational burdens. It supports global health goals by making intelligent and accessible healthcare a reality.
Keywords AI in healthcare, personalized treatment, NLP, ML, hospital management, symptom checker, HER
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-02
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62239
Short DOI https://doi.org/hbdsjz

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