Overview: Artificial Intelligence in Medicine 1st Edition PDF by Niklas Lidströmer & Hutan Ashrafian is a large interdisciplinary reference edited by Niklas Lidströmer and Hutan Ashrafian that examines artificial intelligence across clinical medicine, healthcare research, medical technology, and policy. The 1st Edition provides a scientific foundation for AI in medicine while addressing practical applications, ethical challenges, privacy, diagnosis, patient safety, informatics, and future clinical integration. The first edition provides a structured overview of artificial intelligence across medicine, covering scientific foundations, clinical applications, imaging, informatics, surgery, individualized medicine, privacy, ethics, regulation, safety and future implementation. Its interdisciplinary scope helps clinicians and technical professionals understand both the potential and the limitations of AI systems in healthcare rather than focusing only on algorithms.
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The reference contains 130 entries covering foundational AI concepts, mathematical approaches, the history and development of medical AI, clinical implementation, machine learning, decision support, diagnosis, evidence-based medicine, health informatics, privacy, regulation, ethics, patient safety, and emerging applications across medical specialties. Chapters also examine the social and legal consequences of AI, human oversight, contestability of automated diagnostics, and the challenges of translating algorithms into real healthcare environments. Original illustrations and clinically oriented examples help connect computational concepts with medical practice.
This edition is useful for readers who need artificial intelligence explained within the realities of medicine rather than as a purely computational subject. Medical students and clinicians can build enough technical background to understand how algorithms are developed and evaluated, while researchers and health-technology professionals can examine clinical implementation, safety, ethics, and policy. The breadth of the reference also helps readers compare AI applications across specialties and identify recurring challenges involving bias, privacy, interpretability, validation, and human oversight. It is especially valuable for interdisciplinary work linking medicine, data science, informatics, engineering, and health policy.
Key Coverage & Sections Covered
- Foundations and mathematical concepts of AI
- Machine learning in medicine
- Clinical decision support
- AI-assisted diagnosis
- Medical informatics and health data
- Evidence-based medicine and prediction
- Ethics, regulation, and legal considerations
- Privacy and data protection
Who Uses This Book
- Medical Students (MDs/DOs)
- Physicians
- Clinical Researchers
- Health Informatics Professionals
- Data Scientists Working in Healthcare
- Biomedical Engineers
- Healthcare Leaders
- Medical AI Researchers
- Policy and Ethics Professionals in Digital Health
Also suitable for other healthcare professionals, academic instructors, and advanced learners who need an authoritative reference on Artificial Intelligence in Medicine linking core principles, diagnostic evaluation, and evidence-based practice.
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