Our Research

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.