Our Research
Title: VJenix MVP: Building India’s First AI Health Assistant for Reports, Diagnosis & Future Robotics
Authors:
Dr. Sandeep Ratawal (Co-founder, Strategy & Operations)
Pratyush Agnihotri (Chief Technical Officer & Robotics Head)
Savya Sanchi Mishra (Co-founder, Chief Research Officer & CEO)
Abstract
VJenix is a pioneering AI-based medical assistant developed to democratize healthcare access and empower both doctors and patients. As an MVP (Minimum Viable Product), VJenix offers a multi-modal interaction system that can analyze medical reports, conduct symptom assessments, suggest specialist consultations, and deliver recommendations using AI models through both text and voice. With the inclusion of PDF/X-ray analysis, regional language support, and an eventual robotics integration plan, VJenix aims to revolutionize digital and physical healthcare delivery across India.
1. Introduction
India's vast and diverse population presents a pressing challenge in healthcare access, particularly in underserved regions. The COVID-19 pandemic further highlighted the need for scalable, remote, AI-driven health support systems. VJenix emerges as a response to this gap, aiming to function as an intelligent health assistant that understands regional languages, interprets medical data, and acts as a bridge between patients and healthcare professionals.
This paper outlines the development of VJenix's MVP, designed to deliver report analysis, real-time symptom checking, voice support, and specialist recommendations through AI orchestration. It also sets the foundation for future robotic integration in public spaces like metro stations, rural clinics, and urban kiosks.
2. Methodology
The VJenix MVP is designed using a multi-layered architecture involving:
LLM Integration: GPT-4o, Perplexity, and other APIs for understanding and generating contextual medical responses.
Image and PDF Analysis: APIs such as Qure.ai and Zebra Medical for reading X-rays, MRIs, and DICOM files.
Voice Interaction: Text-to-speech and speech-to-text APIs enabling bilingual conversation (Hindi & English).
Symptom Assessment: AI decision trees and fine-tuned symptom-checker flows mapped with ICD-10 standards.
No Fine-tuning Needed (MVP phase): API orchestration approach with modular plug-ins for report reading and analysis.
3. System Architecture
The MVP runs on a client-server architecture with cloud-based AI APIs at the backend. Key components:
Frontend: React-based interface, integrated with voice & text modules.
Backend: Python/Node.js server connected to external AI APIs (LLMs, Image Analysis, NLP, RAG modules).
Security: SSL, basic encryption & privacy policies for health data compliance.
Storage: Secure AWS/GCP-based object storage for temporary file holding.
The architecture is designed to allow easy transition into Phase 2 where robotics (Jenvik PMA) will interface with this digital system physically.
4. MVP Features & Implementation Plan
Report Analyzer: Upload PDF/X-ray/MRI scans → extract text/images → run through image & text APIs → simplified output
Symptom Checker: User inputs symptoms via text/voice → symptom decision tree + LLM → suggest possible issues + specialist
Voice+Chat Assistant: Human-like conversation interface → responsive in Hindi and English
Language Support: Hindi-English MVP; Tamil and others planned
Deployment Timeline:
Week 1–2: Frontend/backend setup + voice integration
Week 3: Report analysis & RAG plug-in connection
Week 4: Deployment & demo to public
5. Expected Results and Impact
Improved access to health insights for Tier-2/3 cities and rural areas
Reduced burden on doctors by pre-processing reports and symptoms
Layman-friendly, accessible, and bilingual AI health assistant
Foundation for future robotics deployment in kiosks, hospitals, etc.
6. Discussion
VJenix has the potential to become a landmark innovation in India’s digital health ecosystem. Unlike generic AI chatbots, its focus on real-world healthcare delivery through orchestration of specialized AI models makes it practical and scalable. The MVP proves that full model training is not a barrier to innovation if API orchestration is utilized smartly.
Future work includes integrating DICOM viewers, expanding to regional languages, offline functionality, and robotic deployment.
7. References
Qure.ai Imaging AIOpenAI GPT-4o Documentation
Zebra Medical API
WHO ICD-10 Guidelines
India Digital Health Mission (NDHM) policy docs
Disclaimer: This MVP and research document is intended solely for demonstration and early-stage development purposes. The intellectual property and design belong to the founding team of VJenix. Any resemblance or duplication without written consent is strictly prohibited. This document does not constitute a medical device certification.
Title: Integrating AI Chatbots with Medical Robotics for Real-World Deployment
Authors:
Prateek Gaurav, Patna University
Premlata Dubey, Solar Department, Delhi University
Aakash Sinha, Solar Department, Delhi University (Expert in Data Analytics)
Abstract:
This paper explores the integration of artificial intelligence (AI) chatbots with medical robotics, aiming to redefine diagnostics, report analysis, and real-time patient interaction. With the emergence of advanced language models and robotic systems, we propose a multi-modal interface that bridges AI chat-based assistance with physical robotic execution, particularly for semi-urban and rural healthcare deployment. The study analyzes practical pathways, technical constraints, and potential impact.
1. Introduction:
Healthcare accessibility remains a challenge in many parts of the world. AI chatbots have advanced significantly, offering near-human conversational abilities, while medical robotics has demonstrated capabilities in diagnostics, surgical assistance, and health monitoring. Integrating these technologies offers a powerful, scalable healthcare model capable of transforming frontline health services.
2. Literature Review:
AI in Healthcare: Tools like ChatGPT and Med-PaLM have enabled preliminary diagnostics, report reading, and symptom-based guidance.
Medical Robotics: Systems such as Da Vinci, Qure.ai, and AI Doc show potential in diagnostics, while nursing robots are being used for basic hospital tasks in Japan and South Korea.
Challenges in Integration: Key challenges include hardware-software interfacing, natural language grounding in robotic action, and trust in AI-assisted diagnosis.
3. System Architecture:
Our proposed system involves:
Input Layer: Patient interacts via voice or text with the AI chatbot.
Analysis Layer: NLP model processes symptoms or uploads (PDFs, DICOM images, etc.).
Decision Layer: AI provides suggestions or sends instructions to a connected robotic system.
Execution Layer: Robotic interface displays results, suggests actions, or interacts with physical systems (e.g., sample collection or vitals monitoring).
4. Use Cases:
Rural health kiosks with voice-enabled AI bot for check-ups.
Report reading stations where printed/X-ray reports are scanned and explained in local languages.
Early triage units in emergency rooms.
Self-service robotic stations at metro stations or malls.
5. Data Analytics and Feedback Loop:
Led by Aakash Sinha, this module ensures the system learns from user inputs, corrects bias, adapts language tone, and monitors accuracy of suggestions through back-end analytics, logs, and feedback channels.
6. Challenges and Solutions:
Power and Connectivity: Solar-powered kiosks and edge AI models
Language Barriers: Multilingual LLM integration
Medical Accuracy: Tie-ups with certified doctors and regular model fine-tuning
Regulatory Compliance: GDPR, HIPAA, and local health data guidelines adherence
7. Future Scope:
Integration with wearable IoT devices
On-site minor procedure bots guided by AI
Integration with government health databases (ABHA, NDHM)
In-built GenAI feedback to personalize treatment suggestions
8. Conclusion:
The fusion of AI chatbots and medical robotics offers a futuristic yet practical solution to many healthcare challenges. This paper lays the groundwork for scalable, affordable, and intelligent systems that can complement doctors, empower patients, and extend healthcare access across geographies.
Disclaimer:
This research paper is a conceptual and academic exploration. The implementation roadmap, technologies, and strategies are protected under intellectual frameworks. Unauthorized use, replication, or adaptation of this concept without formal consent from the authors and affiliated institutions is not permitted.