Ethical Artificial Intelligence In Modern Medicine: Evidence, Equity, and Accountability for The Modern Clinician

Algorithms are already making decisions about your patients do you know how to evaluate them? From sepsis prediction engines in the ICU to deep learning systems reading chest radiographs, AI-driven tools are actively shaping diagnosis, treatment, and triage across every major specialty. Yet most physicians, residents, and fellows received no formal training in evaluating these systems their validation evidence, their failure modes, their embedded biases, or their legal implications. This 20-chapter textbook closes that gap with a rigorous, clinician-centered framework built for professionals who understand disease and now need to understand the technologies transforming how it is diagnosed and treated. Spanning bioethical theory, regulatory policy, and real-world case analysis across radiology, cardiology, oncology, critical care, primary care, and psychiatry, this comprehensive resource delivers what no single journal article or workshop can: a unified foundation for navigating algorithmic healthcare with competence and confidence. Inside this textbook, you will discover: Master the evaluation of vendor validation studies using CONSORT-AI and SPIRIT-AI reporting standards so you can separate genuine breakthrough from marketing hype Identify algorithmic bias in diagnostic imaging, risk prediction, and treatment recommendation systems and apply structured audit frameworks to detect disparities before they harm your patients Navigate HIPAA, GDPR, and emerging data governance requirements specific to AI-powered health systems with clarity and practical precision Understand the liability landscape when AI recommendations are followed, overridden, or fail including landmark legal cases and malpractice insurance implications Recognize automation bias and deskilling risks in your own practice and implement cognitive strategies that preserve independent judgment Build or participate in institutional AI governance committees using pre-deployment review checklists and post-deployment monitoring protocols Communicate AI-assisted findings to patients through informed consent frameworks that respect autonomy and shared decision-making Your patients deserve a physician who understands the algorithms influencing their care. Scroll up and click Buy Now to build the competency that responsible practice demands.