AI- Powered Disease Diagnosis

Abstract

The \"Data-Driven Healthcare Solutions\" unified digital platform designed to address major issues in the healthcare industry, including delayed emergency response, trouble scheduling timely doctor\'s appointments, and slow and inaccurate symptom detection. With features like a smart chatbot, video and audio consultations, and a centralized health record system, the suggested solution is an AI-powered digital healthcare software that serves as a personal doctor. To cut down on emergency delays, the app would also have special capabilities including an SOS alarm system and ambulance tracking. The team is driven by the desire to employ technology to rectify the inefficiencies and inequities they have seen in the current healthcare system. The software seeks to give both urban and rural residents equal access to healthcare. Through reasonably priced teleconsultations, this strategy would also save expenses, decrease the danger of self-diagnosis, and enhance patient outcomes. The technical approach of the program uses languages and packages such as Python, Flask, scikit-learn, and NLTK, and incorporates a cloud database. Scalability and economic viability are key design features of the platform, which emphasizes an MVP (Minimum Viable Product) strategy.

Introduction

Healthcare faces persistent challenges such as fragmented patient records, slow emergency response, delayed diagnosis, and limited access to specialists—issues that are especially severe in India due to a doctor shortage, uneven resource distribution, and rising self-diagnosis. With only one doctor per 1,511 people and 30% of traffic-accident deaths linked to delayed medical aid, the need for an integrated digital healthcare solution is urgent. The COVID-19 pandemic further revealed gaps in the existing system, as telemedicine use surged but remained fragmented.

The proposed solution is an AI-powered, data-driven healthcare platform that integrates centralized medical records, teleconsultation, ambulance tracking, and an AI symptom-checking chatbot. Using cloud computing, machine learning, and NLP, the platform offers first-level diagnosis, connects patients with appropriate specialists, and supports real-time emergency response. This helps reduce treatment delays, prevent incorrect self-diagnosis, and improve access for underserved rural populations.

The system workflow includes user registration, symptom input, AI-based analysis, specialist recommendation, appointment booking, telemedicine consultations, SOS emergency handling, medical record access, digital prescriptions, and follow-up tracking.

A literature survey highlights recent AI-based healthcare innovations, including symptom-checker chatbots, medical prediction tools, rural healthcare access solutions, and AI medical assistants.

The methodology covers requirement analysis, system design, secure authentication, NLP-based symptom checking, specialist mapping, telemedicine via WebRTC, emergency GPS tracking, OCR-enabled record management, digital prescriptions, patient monitoring, cloud security, and a user-friendly interface. The development process incorporates thorough testing, cloud deployment, and continuous improvement.

Conclusion

A centralized digital healthcare platform creates a patient-centered, economical, and technologically advanced healthcare ecosystem in one location by guaranteeing prompt care, improving emergency response, protecting medical information, and bridging the urban–rural divide. It increases accessibility, empowers both patients and providers, and establishes the groundwork for more intelligent and just healthcare. In the end, such a platform creates the groundwork for a more intelligent, just, and robust healthcare system where everyone, wherever, at any time, can access high-quality care.

References

[1] World Health Organization (WHO). Doctor-to-Patient Ratio Standards. 2023. [2] AIIMS Report. 30% of Accident Victims in India Die Due to Slow Medical Response. 2023. [3] National Crime Records Bureau (NCRB). 42% of Road Accident Deaths Caused by Delays in Reaching Hospitals. 2022. [4] Ministry of Health & Family Welfare (MoHFW), Government of India. Ambulance Availability & Healthcare Infrastructure Data. 2022. [5] McKinsey & NITI Aayog Reports. Digital Health in the COVID-19 Era: Telemedicine Usage Increased by 500%. 2021–2023. [6] World Health Organization. Global Report on Effective Telemedicine Practices. 2023.

Copyright

Copyright © 2025 Aditi D. Desale, Suyog Mankar, Anushka Akolkar, Payal jagdale. 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.