Farhad Soleimanian Gharehchopogh – författare

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.

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.