Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Non-equilibrium in the Cell

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An intelligent lung cancer detection from computed tomography images using robust optimal deep transfer learning with XAI model.

BMC medical informatics and decision making·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment.

Anita Christaline Johnvictor¹, M Poonkodi¹, N Prem Sankar¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.

Journal of Multidisciplinary Healthcare

|November 14, 2024

Summary

This study introduces a secure, AI-powered hospital chatbot using TinyML for efficient patient data processing. It enhances patient care with accurate data recording and improved security through local server operation.

Keywords:

HOG descriptors assistive management autonomous robot edge computing healthcare

More Related Videos

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Jun 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Artificial Intelligence in Healthcare
Edge Computing
Machine Learning for Medical Applications

Background:

Artificial Intelligence (AI) is transforming healthcare, improving patient interactions and satisfaction.
Online AI solutions pose risks to sensitive patient data security.
Advancements in AI impact nursing, diagnostics, and critical medical procedures.

Purpose of the Study:

To develop a secure, AI-driven chatbot for hospital environments.
To address patient data security concerns using local processing.
To improve the efficiency of patient data management and diagnostic support.

Main Methods:

Utilized Edge Computing for secure, on-site data processing.
Implemented Histogram of Gradient (HOG)-based classification for patient identification.
Integrated TinyML for efficient processing of patient data, including temperature measurement and demographic recording via a chatbot.

Main Results:

Achieved 95.8% accuracy in patient detection and 95.3% accuracy for the TinyML model.
Demonstrated rapid on-device processing time (217 ms) and low latency (4 ms).
The TinyML model requires minimal resources (8.8Kb RAM, 50.3Kb Flash memory).

Conclusions:

The developed system offers enhanced security and faster patient data recording compared to existing AI solutions.
All data processing occurs locally, ensuring patient data privacy and integrity.
The AI chatbot facilitates secure data storage for hospital management and diagnostic purposes.