Home Technology Chapter 3 AI for remote patient monitoring in healthcare
Chapter
Licensed
Unlicensed Requires Authentication

Chapter 3 AI for remote patient monitoring in healthcare

  • Rishabha Malviya , Shivam Rajput , Mukesh Roy , Irfan Ahmad and Saurabh Srivastava
Become an author with De Gruyter Brill
Explore this Subject How to publish with us
Artificial Intelligence for Healthcare
This chapter is in the book Artificial Intelligence for Healthcare

Abstract

An enormous rise in the use of artificial intelligence (AI) in healthcare is being seen. Remote patient monitoring, or RPM, is a common healthcare tool that helps doctors keep an eye on patients with long-term or short-term illnesses, who live in remote areas. It can also be used to keep an eye on old people who are getting care at home as well as hospitalized patients. The staff’s efficiency, which is influenced by their duties, is key to the reliability of manual patient monitoring systems. Standard ways of keeping an eye on a patient usually involve uncomfortable methods, which may require touching the patient’s skin to check on their health. The goal of this chapter is to give a full look at RPM systems, covering topics like how new technologies are being used, how AI is changing RPM, and the problems and new trends in AIenhanced RPM. This chapter also talks about the advantages of RPM systems that focus on the patient, and are made possible by Internet of things sensors and wearable tech. Tech like cloud, fog, edge, and blockchain are used in these designs. AI is used in many areas of RPM, such as classifying physical exercise, keeping an eye on long-term illnesses, and checking vital signs in an emergency. This chapter shows how AI-driven RPM systems have changed healthcare tracking applications in a big way. These architectures have been shown to be good at finding early signs that a patient’s health is getting worse and at customizing the tracking of each patient’s health parameters using federated learning. Taking into account the many challenges and new trends in the field, this chapter looks at the possible future paths of AI in RPM uses.

Abstract

An enormous rise in the use of artificial intelligence (AI) in healthcare is being seen. Remote patient monitoring, or RPM, is a common healthcare tool that helps doctors keep an eye on patients with long-term or short-term illnesses, who live in remote areas. It can also be used to keep an eye on old people who are getting care at home as well as hospitalized patients. The staff’s efficiency, which is influenced by their duties, is key to the reliability of manual patient monitoring systems. Standard ways of keeping an eye on a patient usually involve uncomfortable methods, which may require touching the patient’s skin to check on their health. The goal of this chapter is to give a full look at RPM systems, covering topics like how new technologies are being used, how AI is changing RPM, and the problems and new trends in AIenhanced RPM. This chapter also talks about the advantages of RPM systems that focus on the patient, and are made possible by Internet of things sensors and wearable tech. Tech like cloud, fog, edge, and blockchain are used in these designs. AI is used in many areas of RPM, such as classifying physical exercise, keeping an eye on long-term illnesses, and checking vital signs in an emergency. This chapter shows how AI-driven RPM systems have changed healthcare tracking applications in a big way. These architectures have been shown to be good at finding early signs that a patient’s health is getting worse and at customizing the tracking of each patient’s health parameters using federated learning. Taking into account the many challenges and new trends in the field, this chapter looks at the possible future paths of AI in RPM uses.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Cardiovascular disease diagnosis using AI-based imaging 1
  6. Chapter 2 Integration of AI in the management of bone health 23
  7. Chapter 3 AI for remote patient monitoring in healthcare 53
  8. Chapter 4 Engaging AI in emergency medicine for better patient care 91
  9. Chapter 5 Application of AI in ENT (otorhinolaryngology) care 109
  10. Chapter 6 Integration of AI in brain tumor surgery 125
  11. Chapter 7 AI in dentistry: role and application 155
  12. Chapter 8 Managing OPD with AI: implementation and utilization 181
  13. Chapter 9 Elder patient care and monitoring through AI 203
  14. Chapter 10 AI and pregnancy: an unexpected alliance 227
  15. Chapter 11 Implementation of AI in pathology 247
  16. Index 269
https://doi.org/10.1515/9783111549729-003

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Cardiovascular disease diagnosis using AI-based imaging 1
  6. Chapter 2 Integration of AI in the management of bone health 23
  7. Chapter 3 AI for remote patient monitoring in healthcare 53
  8. Chapter 4 Engaging AI in emergency medicine for better patient care 91
  9. Chapter 5 Application of AI in ENT (otorhinolaryngology) care 109
  10. Chapter 6 Integration of AI in brain tumor surgery 125
  11. Chapter 7 AI in dentistry: role and application 155
  12. Chapter 8 Managing OPD with AI: implementation and utilization 181
  13. Chapter 9 Elder patient care and monitoring through AI 203
  14. Chapter 10 AI and pregnancy: an unexpected alliance 227
  15. Chapter 11 Implementation of AI in pathology 247
  16. Index 269
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2026 De Gruyter Brill
Downloaded on 2.2.2026 from https://www.degruyterbrill.com/document/doi/10.1515/9783111549729-003/html
Scroll to top button