Generative Artificial Intelligence and Ethics for Healthcare.

Format:

Book

Author/Creator:

Gaur, Loveleen.

Contributor:

Abraham, Ajith, 1968-

Language:

English

Physical Description:

1 online resource (284 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Generative Artificial Intelligence and Ethics for Healthcare conducts a deep dive into the potential issues and challenges associated with Generative AI applications.The book begins with foundational concepts of generative AI and then explores ethical theories, including specific case studies in healthcare, and concludes with discussions on.

Contents:

Front Cover

Front Matter

Titlepage

Copyright

Contents

Chapter 1 Generative AI in healthcare: Introduction, concept, applications, and challenges

1.1 Introduction

1.1.1 Evolution, development, and notable generative AI technologies

1.1.2 Timeline of generative AI (See Table 1.1)

1.1.3 Some notable generative AI (See Table 1.2)

1.2 How generative AI differs from other AI approaches

1.2.1 Core principles and underlying algorithms (See Table 1.3)

1.3 Applications of generative AI in healthcare

1.3.1 Medical imaging and diagnostics

1.3.2 Drug discovery and development

1.3.3 Personalized treatment planning

1.3.4 Health data generation and augmentation

1.3.5 Virtual patient modeling and simulation

1.3.6 Clinical administration support

1.3.7 Clinical decision-making

1.3.8 Clinical trial optimization

1.3.9 Healthcare operations and resource Management

1.3.10 Chatbots

1.3.11 Medical triage

1.3.12 Virtual rehabilitation

1.3.13 Adverse drug reactions (ADRs)

1.3.14 Biomarker identification

1.3.15 Synthetic patient records from healthcare data

1.3.16 Medical education

1.4 Challenges and limitations of generative AI in healthcare

1.4.1 Attribution problem

1.4.2 Contextualization

1.4.3 Data quality and bias

1.4.4 Patient data privacy

1.4.5 Generation of false information

1.4.6 Integration with existing technology

1.4.7 Cost

1.4.8 Lack of professional expertise

1.4.9 Ethics

1.5 Conclusion and future directions

References

Chapter 2 Understanding training data and mitigating biases in training data

2.1 Introduction to sources of bias in training data

2.1.1 Bias in data collection processes

2.2 Types of bias in healthcare data

2.2.1 Demographic bias

2.2.2 Historical bias

2.2.3 Labeling bias

2.2.4 Sampling bias.

2.2.5 Algorithmic bias

2.3 Techniques for identifying bias

2.3.1 Statistical methods for bias detection

2.3.2 Algorithmic auditing and bias assessment tools

2.4 Mitigation strategies for biased training data

2.4.1 Data augmentation techniques

2.4.2 Bias-aware algorithm design

2.4.3 Bias mitigation through algorithmic fairness interventions

2.5 Caselet: A diagnostic AI in a multicultural hospital

2.5.1 Background

2.5.2 Sequence of events leading to the ethical conflict

2.5.3 Ethical analysis of the scenario

2.5.4 Relevant ethical principles

2.5.5 Potential Solutions and their evaluation1

2.6 Conclusion

Chapter 3 Calibrating generative AI models for healthcare

3.1 Introduction

3.1.1 Understanding model calibration

3.1.2 Biases in generative AI models

3.1.3 Significance of calibration in mitigating biases

3.1.4 Calibration methods

3.1.5 Implementing calibration in healthcare AI

3.1.6 Evaluating calibration performance: Metrics and visualization

3.1.7 Case studies: Bias reduction through calibration

3.1.8 Challenges in calibrating healthcare AI models

3.1.9 Ethical considerations

3.1.10 Future directions in calibration for healthcare AI

3.2 Conclusion

Chapter 4 Explainability in generative AI and large language models

4.1 Introduction to explainability

4.1.1 Relevance of generative AI and LLMs

4.2 Challenges of explainability in generative AI

4.2.1 Complexity of models

4.2.2 Black box nature

4.2.3 Tradeoffs

4.3 Techniques for enhancing explainability

4.3.1 Model-agnostic methods

4.3.2 Local interpretable model-agnostic explanations

4.3.3 SHapley additive explanations

4.3.4 Intrinsic explainability approaches

4.4 Interpreting outputs of LLMs

4.5 Bias detection.

4.6 Tools and frameworks for explainability in generative AI

4.6.1 AI explainability 360

4.6.2 Captum

4.6.3 ELI5

4.7 Custom visualization techniques

4.8 Attention heatmaps for transformer models

4.9 Extracting decision rules from neural networks

4.10 Ethical considerations

4.10.1 Transparency vs. privacy

4.10.2 Explainability in sensitive applications

4.10.3 Accountability and trust

4.11 Future directions in explainability for generative AI

4.11.1 Advances in research

4.11.2 Explainability in real-time applications

4.11.3 Integration with responsible AI

Chapter 5 Ethical considerations in generative AI development and usage

5.1 Introduction: The ethical landscape of generative AI

5.1.1 Defining ethics in the context of generative AI

5.1.2 Why do ethical considerations matter?

5.2 Developer's perspective: Challenges and ethical responsibilities

5.2.1 Navigating ethical challenges in AI development

5.2.2 The importance of transparency in AI

5.2.3 Ethical data usage: Sourcing and consent

5.3 Potential biases and their impact

5.3.1 How biases arise in generative AI

5.3.2 Strategies for mitigating biases

5.3.3 Case studies: Bias in generative AI

5.4 Implications for users: Interacting with generative AI applications

5.4.1 User awareness and education

5.4.2 Privacy concerns in user data collection and processing

5.4.3 Transparency in user interactions

5.5 In-depth exploration of ethical issues in generative AI

5.5.1 Ethical dilemmas in AI creativity

5.5.2 The role of AI in society: Ethical impacts

5.5.3 Governance and regulation of generative AI

5.6 Future directions: Building an ethical framework for generative AI

5.6.1 Developing ethical guidelines for AI developers

5.6.2 Promoting ethical AI through collaboration

References.

Chapter 6 Ethical concerns of generative AI in healthcare applications

6.1 Introduction: The promise and perils of generative AI in healthcare

6.1.1 The rise of generative AI in healthcare

6.1.2 Why ethics matter more in healthcare

6.2 The ethical landscape of generative AI in healthcare

6.2.1 Patient data privacy and confidentiality

6.2.2 Bias and fairness in AI-driven healthcare

6.2.3 Transparency and explainability in AI medical decisions

6.3 Specific ethical risks in healthcare applications

6.3.1 AI in diagnostics: Accuracy vs. over-reliance

6.3.2 Generative AI in personalized medicine

6.3.3 AI in drug development and clinical trials

6.4 Case studies: Ethical dilemmas in AI-driven healthcare

6.4.1 Case study: NCI's use of generative AI in cancer diagnosis - ethical considerations and human oversight

6.4.2 Case study: Bias in AI-driven health monitoring

6.4.3 Case study: Privacy breaches in AI-powered health apps

6.5 Addressing ethical concerns: Best practices and guidelines

6.5.1 Implementing ethical AI in healthcare

6.5.2 Enhancing transparency and accountability

6.5.3 Building trust with patients and healthcare providers

6.6 The future of ethical generative AI in healthcare

6.6.1 Emerging ethical frameworks and regulations

6.6.2 The role of collaboration in ethical AI development

6.6.3 Toward a responsible future: Ethical AI for all

Chapter 7 Ethical concern of data privacy and patient data ownership

7.1 Introduction: The dual-edged sword of generative AI in healthcare

7.1.1 The promise of generative AI for patient care

7.1.2 The rising concerns over data privacy and ownership

7.2 Understanding data privacy in the age of generative AI

7.2.1 Defining data privacy in healthcare

7.2.2 How generative AI utilizes patient data.

7.2.3 The risks of data breaches and unauthorized access

7.3 Patient data ownership: Ethical and legal perspectives

7.3.1 What does patient data ownership entail?

7.3.2 The conflict between AI development and data ownership

7.3.3 Ethical implications of data monetization

7.4 Evaluating the effectiveness of privacy laws

7.4.1 Overview of HIPAA and other relevant privacy laws

7.4.2 How HIPAA applies to generative AI in healthcare

7.4.3 Gaps and limitations in current privacy laws

7.5 Case studies: Navigating data privacy and ownership in healthcare AI

7.5.1 Case study: Data breach in AI-powered health monitoring

7.5.2 Case study: Disputes over patient data ownership

7.5.3 Case study: AI and data monetization in healthcare

7.6 Addressing ethical concerns

7.6.1 Developing robust data privacy frameworks

7.6.2 Enhancing patient control over data

7.6.3 The role of transparency and accountability

7.7 The future of data privacy and ownership in generative AI

7.7.1 Emerging trends in privacy-enhancing technologies

7.7.2 Legal and ethical reforms for the AI era

7.7.3 Building trust in AI-driven healthcare

Chapter 8 Trust, accountability, and informed consent: Cornerstones of ethical practice in clinical medicine

8.1 Introduction: The ethical foundations of clinical medicine

8.1.1 The critical role of ethics in patient care

8.1.2 Why trust, accountability, and informed consent matter?

8.2 Trust in clinical medicine

8.2.1 The Importance of Trust in Healthcare Relationships

8.2.2 Factors that influence patient trust

8.2.3 The consequences of eroded trust

8.3 Accountability in clinical practice: Ensuring ethical responsibility

8.3.1 Defining accountability in healthcare

8.3.2 The role of accountability in ethical decision-making.

8.3.3 Mechanisms for enforcing accountability.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9780443331244

OCLC:

1537545821