Applications of Large Language Models (LLM) in Healthcare Systems
Opportunities, Challenges, and Ethical Considerations
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Applications of Large Language Models (LLM) in Healthcare Systems
Opportunities, Challenges, and Ethical Considerations
About this book
This book offers a comprehensive exploration into the role of Large Language Models (LLMs) in modern healthcare. It focuses specifically on the lifecycle of LLM deployment in healthcare settings, including transparency, accountability, data privacy, and regulatory compliance to ensure safe and effective use.
By bridging the gap between technical artificial intelligence (AI) development and clinical application, this book highlights the critical collaboration between clinicians and data scientists to create representative datasets and fine-tune models for clinical accuracy and interpretability. Real-world challenges such as mitigating bias, managing AI hallucinations, and safeguarding patient confidentiality are explored, alongside strategies for continuous improvement and long-term impact assessment.
Key features include:
- Case studies illustrating LLM applications in clinical decision support, medical imaging, patient communication, and administrative automation
- In-depth discussion of data privacy, regulatory compliance, and ethical considerations in AI healthcare applications
- Insights into overcoming challenges like bias, hallucinations, and interoperability with existing health information systems
- How LLMs could revolutionize patient care in future, including operational efficiency and personalized medicine
This book is an essential resource for clinicians, healthcare executives, technologists, data scientists, and students seeking to harness the power of LLMs to improve patient outcomes and streamline healthcare delivery.
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Information
Table of contents
- Cover
- Half-Title
- Title
- Copyright
- Contents
- Preface
- List of Contributors
- Editor bios
- 1 Large language models in clinical applications
- 2 Administrative efficiency: Providing healthcare operations
- 3 LLMs in drug discovery, clinical trial analysis, and medical literature review
- 4 LLMs for personalized medicine and treatment planning
- 5 The large language models for public health surveillance and outbreak
- 6 The role of large language models in delivering artificial intelligence-based psychological interventions and therapies
- 7 Explainable AI (XAI): Making LLM decisions transparent and trustworthy
- 8 Ethical implications for LLMs
- 9 Limitations of large language models in healthcare systems
- 10 Future trends and innovations in healthcare systems using LLMs
- Index