Artificial Intelligence in Patient Counselling.

Format:

Book

Author/Creator:

Prajapati, Bhupendra G.

Language:

English

Subjects (All):

Artificial intelligence.

Patient education.

Physical Description:

1 online resource (575 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Artificial Intelligence in Patient Counselling investigates how AI is revolutionizing patient counseling and education within healthcare.This book provides a thorough look at innovative AI tools transforming the patient experience, from streamlining communication to tailoring treatment plans.

Contents:

Front Cover

Front Matter

Titlepage

Copyright

Dedication

Contents

Contributors

Editor Bio

Preface

Acknowledgments

Chapter 1 Introduction to artificial intelligence in patient counseling: Challenges and future directions in artificial intelligence-based patient education

1.1 Introduction

1.1.1 Emergence of artificial intelligence in healthcare communication

1.2 Fundamentals of artificial intelligence in patient counseling

1.2.1 Definition and core concepts: Machine learning, natural language processing, and expert systems

1.2.2 Types of artificial intelligence tools used in education and decision support

1.3 Benefits of artificial intelligence in patient education and engagement

1.3.1 Personalized content delivery

1.3.2 24/7 accessibility and patient empowerment

1.3.3 Support for low-health-literacy populations

1.3.4 Improved adherence and clinical outcomes

1.4 Challenges in artificial intelligence-driven patient counseling

1.4.1 Data quality and bias in artificial intelligence models

1.4.2 Lack of emotional intelligence in artificial intelligence agents

1.4.3 Ethical and legal concerns (e.g., informed consent, misinformation)

1.4.4 Patient trust and digital literacy barriers

1.4.5 Integration challenges in clinical workflows

1.5 Data privacy and security concerns

1.6 Future directions and innovations

1.7 Conclusion

Declaration of competing interest

Declaration of generative AI and AI-assisted technologies in the writing process

Data availability

References

Chapter 2 Integrated artificial intelligence in patient counseling: A comprehensive guide for bioengineers and medical professionals

2.1 Introduction

2.1.1 Artificial intelligence's place in contemporary healthcare.

2.1.2 Importance of artificial intelligence in patient counseling

2.1.3 Scope and objectives

2.2 Artificial intelligence's fundamentals in healthcare

2.2.1 An overview of the many artificial intelligence technologies

2.2.2 Artificial intelligence-powered solutions for decision support

2.2.3 Artificial intelligence in personalized medicine

2.3 Artificial intelligence-powered patient interaction and outreach

2.3.1 Artificial intelligence chatbots for patient counseling

2.3.2 Assistants for virtual health

2.3.3 Speech and emotion recognition for personalized interactions

2.4 Artificial intelligence-driven virtual and digital health counseling

2.4.1 Telemedicine and artificial intelligence-powered virtual consultations

2.4.2 Artificial intelligence-integrated mobile applications for patient support

2.4.3 Wearable artificial intelligence for real-time patient monitoring

2.5 Artificial intelligence-driven customized therapy planning and compliance enhancement

2.5.1 Artificial Intelligence in Medication Management and Adherence Monitoring

2.5.2 Predictive analytics for treatment options

2.5.3 Artificial intelligence-enabled behaviors change strategies

2.6 Legal, ethical, and regulatory factors

2.6.1 Healthcare artificial intelligence data privacy

2.6.2 Ethical challenges in artificial intelligence-powered patient counseling

2.6.3 Compliance frameworks for artificial intelligence integration in healthcare

2.7 Challenges and future directions

2.7.1 Integration of clinical workflows presents challenges

2.7.2 Artificial intelligence bias and equity consideration in patient counseling

2.7.3 Future innovations and research opportunities

2.8 Case studies and practical implementations

2.8.1 Artificial intelligence in oncology patient counseling.

2.8.2 Artificial intelligence in chronic disease management

2.8.3 Artificial intelligence in counseling for mental health

2.9 Implementation strategies for bioengineers and medical professionals

2.9.1 Designing artificial intelligence systems for patient-oriented care

2.9.2 Collaborative approaches between engineers and clinicians

2.9.3 Training and skill development for artificial intelligence-enabled healthcare

2.9 Summary

Chapter 3 The evolution of healthcare and artificial intelligence integration

3.1 Introduction to healthcare evolution

3.1.1 Key milestones overview of healthcare systems from ancient times to modern medicine

3.1.2 Medical advancements

3.1.3 The transition from traditional to technology-driven healthcare

3.2 Historical perspective of healthcare development

3.2.1 Ancient medical practices: Ayurveda, traditional chinese medicine, and hippocratic medicine

3.2.2 Evolution of hospitals and healthcare infrastructure

3.2.3 Introduction of evidence-based medicine and clinical research

3.3 Technological advancements in healthcare

3.3.1 Impact of medical imaging: X-Rays, CT scans, and magnetic resonance imaging

3.3.2 Role of robotics and minimally invasive surgery

3.3.3 Breakthroughs in personalized medicine and genomics

3.4 The digital revolution in healthcare

3.4.1 Introduction of electronic health records

3.4.2 Big data and predictive analytics in healthcare

3.5 Fundamentals of artificial intelligence in healthcare

3.5.1 Artificial intelligence: Definition and fundamental concepts

3.5.2 Artificial intelligence in medicine: Applications and use cases

3.5.3 Artificial intelligence versus conventional computing in healthcare

3.6 Artificial intelligence applications in diagnosis and treatment.

3.6.1 Building bridges: An analysis of artificial intelligence, traditional medicine, and the future

3.6.2 Artificial intelligence-assisted drug discovery and repurposing

3.6.3 Personalized medicine and predictive analytics in artificial intelligence

3.7 Artificial intelligence in patient care and hospital management

3.7.1 Chatbots for patient interaction: Powered by artificial intelligence

3.7.2 Artificial intelligence-powered robotic surgeries and automation in healthcare

3.7.3 Artificial intelligence for optimizing hospital resource allocation

3.7.4 Leveraging artificial intelligence to enhance clinical decision-making

3.8 Challenges and ethical considerations in artificial intelligence-integrated healthcare

3.8.1 The role of bias in artificial intelligence models and the danger of misdiagnosis

3.8.2 Data privacy and security concerns

3.8.3 Ethical concerns and accountability in artificial intelligence decision-making

3.9 Future perspectives and innovations in artificial intelligence-driven healthcare

3.9.1 Artificial intelligence and wearable technology in preventive healthcare

3.9.2 Integration of artificial intelligence with blockchain for secure health data management

3.9.3 Artificial intelligence's role in global health crisis management (e.g., pandemics)

3.10 Conclusion

3.11 Future scope

Chapter 4 Understanding machine learning for patient education

4.1 Introduction to machine learning in healthcare

4.1.1 Evolution and adoption in medicine

4.1.2 Types of machine learning relevant to patient education

4.1.3 Applications of machine learning in patient education

4.1.4 Key algorithms and models used

4.1.5 Data requirements and challenges

4.1.6 Success stories and lessons learned

4.1.7 Future trends and innovations

4.2 Conclusion

Acknowledgment.

Abbreviations

Chapter 5 Natural language processing in healthcare communication

5.1 Introduction

5.1.1 Overview of natural language processing in healthcare

5.1.2 Importance of effective communication in healthcare

5.1.3 Applications of natural language processing in healthcare communication

5.2 Predictive modeling for patient care

5.3 Technological advances in natural language processing for healthcare

5.4 Recent developments in natural language processing algorithms

5.5 Integration of artificial intelligence and machine learning

5.5.1 Case studies of successful implementations

5.5.2 Clinical applications of natural language processing

5.6 Challenges and ethical considerations

5.6.1 Data privacy and security

5.6.2 Data-related challenges

5.7 Technical challenges

5.7.1 Domain adaptation

5.7.2 Interpretability of models

5.7.3 Bias and fairness in artificial intelligence models

5.8 Ethical considerations

5.8.1 Patient privacy and confidentiality

5.8.2 Informed consent and data ownership

5.9 Regulatory and compliance issues

5.10 Mitigation strategies and recommendations

5.10.1 Explainable artificial intelligence

5.10.2 Inclusive and representative training data

5.11 Future directions

5.11.1 Emerging trends in natural language processing and healthcare

5.11.2 Potential impact on healthcare outcomes

5.11.3 Opportunities for further research

5.11.4 Regulatory and implementation considerations

5.12 Conclusion

5.13 Future scope

5.14 Final thoughts on the role of natural language processing in healthcare communication

Author contributions

Competing interests

Availability statement

Chapter 6 Chatbots and virtual health assistants in patient counseling

6.1 Introduction.

6.2 Types of chatbots and virtual health assistants.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-443-43822-6

9780443438226

OCLC:

1549669466