About this book:

This book provides a comprehensive, clinician‑focused guide to developing, validating, and deploying artificial intelligence in medical practice. It begins by stressing that successful clinical AI must start with a clearly defined, actionable clinical question and proceeds through the foundations of healthcare data—EHRs, imaging, sensor streams, and omics—addressing data quality, bias, and fairness as ethical and safety imperatives. Subsequent chapters cover feature engineering and representation learning, supervised learning for diagnosis and prognosis, unsupervised and self‑supervised methods, time‑series modeling for physiological data, natural language processing for clinical text, and medical imaging AI, each emphasizing the translation of technical methods into clinically useful, interpretable tools.

The text then moves to the practical aspects of building reliable AI systems: predictive risk scores and early warning systems, causal inference and counterfactual reasoning, interpretability and explainability techniques, uncertainty quantification and out‑of‑distribution detection, and rigorous validation strategies (internal, external, transportability). It discusses prospective study designs, clinical trials, and impact evaluation to demonstrate real‑world benefit, and addresses human factors, workflow integration, user experience, safety, robustness, and post‑deployment monitoring. Regulatory pathways (FDA, EU MDR, global frameworks), privacy, security, and ethical stewardship of health data are examined, followed by guidance on multidisciplinary teamwork, governance, implementation science, change management, reimbursement, procurement, sustainable business models, equity, access, and community‑engaged AI.

Finally, the book consolidates these concepts through detailed case studies—sepsis early warning, diabetes risk prediction, diabetic retinopathy screening, and drug repurposing—and offers a clinician’s roadmap for selecting, evaluating, and leading AI projects. Throughout, the emphasis remains on patient safety, equitable care, transparent and clinically legible models, and the integration of AI as a trustworthy aid that augments rather than replaces professional judgment. The work serves as a practical manual for clinicians, data scientists, informaticians, and health‑system leaders seeking to turn promising algorithms into dependable, ethically sound instruments at the bedside.

What You'll Find Inside:

• How to translate clinical needs into actionable AI questions that are specific, measurable, and tied to interventions.
• Understanding the diverse sources of healthcare data (EHRs, imaging, sensors, omics) and their quality, bias, and preprocessing challenges.
• Strategies for detecting, mitigating, and monitoring bias and fairness throughout the AI lifecycle to ensure equitable care.
• Rigorous validation approaches including internal, external, transportability, prospective studies, and post-deployment monitoring.
• Practical guidance on integrating AI into clinical workflows, addressing human factors, interpretability, safety, regulatory pathways, and sustainable implementation.

Who's It For:

The book is intended for clinicians, data scientists, informaticians, and health system leaders who are involved in developing, evaluating, or deploying AI in healthcare and who seek a pragmatic, patient‑centered roadmap that balances technical rigor with safety, equity, and real‑world impact.