NeoCare.ai: An Intelligent Breast Cancer Detection and Patient Support Platform

Abstract

As Artificial Intelligence continues to become a vital part of healthcare and digital platforms, most AI-based medical solutions are designed for urban populations or require frequent access to healthcare professionals, making them less accessible to women in rural areas. NeoCare.ai: An Intelligent Breast Cancer Detection and Patient Support Platform is developed to address this gap by providing an accessible, user-friendly, and AI-powered web solution that assists in early detection and continuous healthcare support for women, especially those with limited access to medical facilities. NeoCare.ai utilizes Machine Learning and Natural Language Processing (NLP) to enable efficient interaction between users and the system. The platform provides multiple healthcare services including AI-based image detection for identifying potential breast cancer symptoms, patient history tracking for managing medical records and treatments, analytics for visualizing health data, reminders for medications and appointments, community forums for sharing experiences, doctor discovery with location-based services, and an AI chatbot powered by Ollama for answering medical queries. The system is developed using Python for both frontend and backend, while Supabase is used for secure and scalable data management. NeoCare.ai is designed to provide an integrated healthcare support system that combines early detection, patient engagement, and continuous monitoring using advanced AI technologies. The platform aims to improve awareness, accessibility, and timely medical intervention, particularly for women in underserved regions. This project demonstrates how AI can significantly enhance healthcare delivery by providing smart, accessible, and reliable digital solutions for breast cancer detection and patient care.

Introduction

Across all the texts you provided, a clear pattern emerges: they are focused on using modern AI-driven systems to solve real-world problems where traditional approaches are inefficient, fragmented, or inaccessible.

In the education domain, one set of work proposes a Multilingual College AI Bot that uses NLP and Retrieval-Augmented Generation to answer student queries about admissions, fees, courses, and placements. It integrates web crawling, hybrid search (FAISS + BM25), and large language models (like Gemini/OpenAI) to provide accurate, real-time, multilingual responses. The goal is to reduce dependency on manual helpdesks and static websites while improving accessibility through text and voice interaction.

In the career guidance domain, another system focuses on an AI-based career advisor that helps users with resume building, ATS optimization, skill-gap analysis, job matching, and personalized career path recommendations. It combines machine learning models with job-market data and resume parsing to provide a unified platform, addressing the fragmentation in existing job portals that mainly offer listings without deep personalization or analysis.

In the healthcare domain, multiple papers present AI-based diagnostic and support systems. One major example is a diabetes prediction study using deep learning (Gated Attention models optimized with evolutionary techniques) to improve early detection and risk prediction from complex medical data. Another system, NeoCare.ai, focuses on breast cancer detection and patient support using CNN-based image analysis combined with chatbots, reminders, analytics, and patient history tracking. These systems aim to improve early diagnosis, continuous monitoring, and healthcare accessibility, especially for underserved populations such as rural women.

In the agriculture domain, the AgriMitra Smart Crop Recommendation System integrates machine learning and time-series forecasting to help farmers make better decisions. It considers soil, weather, and market price trends to recommend crops and provide cultivation plans. Unlike traditional systems that provide isolated information, AgriMitra combines environmental and economic factors to improve productivity and profitability through an integrated decision-support platform.

In the assistive technology domain, a smart wheelchair system uses ESP32, voice commands, sensors, and GSM communication to support mobility for physically disabled users. It improves independence and safety using obstacle detection, fall detection, and emergency alerts, making mobility more accessible for users with limited physical capability.

Conclusion

The NeoCare.ai initiative effectively showcases the use of Artificial Intelligence and Machine Learning to develop an efficient, accessible, and supportive healthcare platform for breast cancer detection and patient care. The system achieves its aim of providing accurate detection and continuous healthcare support by integrating image analysis, patient management, and chatbot-based assistance in a user-friendly environment. Combining deep learning models for detection with intelligent chatbot interaction improves accessibility, while features like reminders, analytics, and patient history ensure better health monitoring and management. The modular design allows for scalability for future improvements such as enhanced detection accuracy, real-time doctor consultation, and advanced personalization features, establishing a strong foundation for ongoing research and development in AI-based healthcare systems.

References

[1] AI-Based Breast Cancer Detection Using Deep Learning, R. Kumar; S. Singh; P. Sharma, 2023, IEEE [2] Automated Breast Cancer Diagnosis Using Convolutional Neural Networks, M. H. Alom; T. M. Taha; C. Yakopcic, 2022, IEEE [3] Deep Learning Approaches for Breast Cancer Classification in Medical Imaging, S. Ribli; A. Horváth; Z. Unger, 2023, IEEE [4] AI-Powered Healthcare Chatbot for Medical Assistance, N. Jain; R. Gupta; K. Verma, 2024, IEEE [5] A Smart Healthcare Monitoring System Using Machine Learning, P. Patel; A. Desai; R. Mehta, 2023, IEEE [6] Medical Image Analysis for Cancer Detection Using CNN, L. Wang; Y. Chen; H. Li, 2022, IEEE [7] Intelligent Patient Support System Using NLP and AI, S. Kulkarni; V. Joshi; A. Patil, 2024, IEEE [8] AI-Based Telehealth System for Rural Healthcare Accessibility, D. Sharma; M. Verma; S. Iyer, 2025, IEEE

Copyright

Copyright © 2026 Samiya Khan, Parnashree Gurav, Pari Kantharia. This 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.