Overview

Building Trustworthy AI for Better Healthcare Outcomes Principled Artificial Intelligence in Medicine refers to the development and use of AI systems in healthcare guided by core ethical and clinical standards: fairness, transparency, accountability, safety, and patient-centeredness. It emphasizes building models that are clinically validated, free from harmful bias, explainable to clinicians, and compliant with privacy and regulatory requirements. The approach integrates technical rigor with governance, risk management, and human oversight to ensure AI supports, not replaces, clinical judgment. It covers applications from diagnostics to decision support while addressing data quality, deployment, and monitoring. The goal is to harness AI's potential to improve outcomes while maintaining trust, equity, and professional responsibility across all healthcare stakeholders. Why This Book Matters Now - Practical & Principled: Translates AI ethics, fairness, transparency, and accountability into actionable frameworks for real-world healthcare settings - Stakeholder-Aligned: Addresses the distinct needs and responsibilities of clinicians, hospital leaders, developers, regulators, researchers, and patients - Clinically Grounded: Focuses on AI applications in diagnosis, decision support, imaging, predictive analytics, and workflow optimization with evidence-based evaluation - Regulation-Ready: Aligns with emerging global standards from FDA, EMA, WHO, and other bodies shaping medical AI oversight Inside You'll Learn - Core Principles: Explainability, bias mitigation, data governance, privacy, and human oversight in medical AI - Clinical Integration: Best practices for validating models, monitoring performance post-deployment, and managing human-AI collaboration - Risk & Safety: Strategies for identifying failure modes, managing liability, and ensuring patient safety in high-stakes environments - Implementation Roadmap: How to build governance structures, assess readiness, and scale AI responsibly across health systems - Future Directions: The role of generative AI, foundation models, and federated learning in advancing personalized medicine Who This Book Serves - Clinicians & CMIOs: Evaluate and adopt AI tools with confidence and clinical rigor - Hospital Executives & Policymakers: Build governance and compliance programs that meet evolving standards - Developers & Data Scientists: Design models that are technically robust and ethically sound for healthcare use - Researchers & Educators: Understand the intersection of AI, medicine, and policy to guide future innovation Clear, actionable, and forward-looking, _Principled Artificial Intelligence in Medicine_ equips healthcare stakeholders to harness AI's potential while upholding the highest standards of patient care and professional responsibility.

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