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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63887
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Master of computer application
Bapuji institute of engineering and technology Davanagere
Master of computer application
Bapuji institute of engineering and technology Davanagere
Across the planet average human life expectancy is increased constantly we need more effective ways to take care for elderly people. Doctors and nurses get massive articles like medical records smart watches data daily activity log and so on. The amount of data is way too much to read and analyse by hand. It makes identification of issues and care provision challenging and time-consuming. The automatic reading and classifying of all this health data will be made possible by Artificial Intelligence The project “AI Driven Health Care for Older Adults” aims at developing a simple processor system help older adults with AI. Moving the focus of care from reaction to prediction is our goal. The system essentially consists of two main components. The first component is the prediction tool. A tool powered by AI assesses the information extracted from a patient’s health record. The model predicts the odds a person will fall get confused or get readmitted to a hospital. The second Care Helper provides them with personalized advice using these forecasts. One such case of a decision would be a doctor changing the dosage of a medicine or giving one particular exercise or telling a care worker that today the patient will need more care.
KEYWORDS
would have far less accessibility .By understanding the intent and generating responses smartly coupled with pre-processing information efficiently the chatbot is adaptable to various real-world situations including but not limited to noisy voice input and ambiguous short messages and low digital literacy among others. The system We utilize conversational AI and intelligent agent technology to create an effective AI-driven Healthcare chatbot for Older Adults that provides accessible personalized and scalable health support to the connected ageing population. Our system will include the Natural Language Understanding components and interfaces using voice and text together with personal health data to provide advice reminders triage and emergency detection to the users .The AI- Driven Healthcare Chatbot for Older Adults shows just how a health conversation agent that is supervised manually can provide expert health information at any time from anywhere on demand. It is also a natural alternative to keeping manual supervision and hotlines. A manual supervision system of nursing and a conventional telephone hotline encourages easy interaction and clear operations for the elderly people while optimally alerting the caregivers and health professionals for collective review with regards to optimisation of shared care. The system designed shall ensure faultless assistance to the user at low latency and proper resources utilization is ensured. This solution can therefore be successfully deployed in home healthcare assisted living community health and many other such settings. This project shows the increasing demand for explainable AI with privacy preservation in healthcare applications. The chatbot offers the user certain trust regarding secure authentication data minimization and visible decision-logging functions in order to be able to protect their data. The design will be modular; adaptation in neighbouring domains is thus easy. For example EHRs telemedicine wearable sensors etc. will seamlessly connect and foster further adoption across the healthcare workflows.In the end the AI-Driven Healthcare Chatbot for Older Adults has achieved what it set out to do with a strong and user-centered design that strikes a perfect balance between accuracy usability and safety. It lays the foundation for future upgrades to include totally integrated multimodal interactions: voice + speech by video; continuous learning with increased personalization; rigorous clinical trials to confirm these innovations may have unexpected side effects; and tighter system integration so it would fit seamlessly into the daily lives of health service providers everywhere.
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Publication Date
5/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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