Artificial Intelligence on Medical Data

Artificial Intelligence on Medical Data by invites clinicians, data scientists, and healthcare leaders into the fast-moving intersection of technology and patient care. This authoritative guide captures how modern artificial intelligence and machine learning transform medical records, imaging, and real‑world data into actionable insights that improve outcomes and reduce costs.

Start with clear, real‑world explanations of core techniques—supervised and unsupervised learning, deep learning for imaging, natural language processing for clinical notes—and progress to practical applications such as predictive analytics, clinical decision support, and population health management. Concise case studies illustrate deployments across hospitals and clinics from New York to Nairobi to Tokyo, making the content globally relevant and GEO-friendly.

Readers will appreciate the practical focus: data preprocessing for electronic health records (EHR), model validation, explainability, and strategies to manage bias and ensure reproducibility. The book also addresses legal and ethical considerations, including privacy and regulatory frameworks like HIPAA and GDPR, helping teams translate AI pilots into safe, compliant production systems.

Whether you’re a physician seeking to interpret algorithmic outputs, a researcher building robust models from noisy clinical data, or an administrator evaluating AI investments, this book provides the tools and perspectives to act confidently. Packed with actionable insights and industry best practices, it’s an essential resource for anyone shaping the future of healthcare with data.

Order your copy today and bring cutting‑edge AI strategies to your medical practice, research lab, or health system.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.