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Machine Meets Medicine: Building Responsible AI in Healthcare
Paperback
Technology & EngineeringGeneral Computers
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Publisher: Apress
Language: English
In modern healthcare, even the most accurate AI models fail when they meet the realities of human life. Machine Meets Medicine begins with a stark example. A neonatal seizure algorithm attributes an infant's seizures to birth asphyxia and never reaches the real cause, a profound hypoglycemia produced by the local practice of discarding colostrum. No Electronic Health Record was built to record that practice. The model was not technically flawed, it had simply entered a world shaped by silent data, the social and clinical factors that never reach a structured dataset.
For AI architects, technical leaders, and senior data scientists, the challenge is no longer accuracy. It is building systems that survive real-world complexity. This book bridges code and clinical care with a practical playbook for safety, governance, and implementation. Each chapter closes with a working instrument, a procedure you can run on Monday against a system you already own. The Silence Audit surfaces the determinants your dataset was never going to contain. The Validation Ladder carries a model from retrospective testing through shadow mode to silent trials without skipping a rung. Each instrument arrives with an honest account of what it cannot see, because a method that claims to catch everything is the first thing that will fail you.
Whether you're navigating FDA and EU regulation or arriving from a technical background, Machine Meets Medicine is a field guide to the distance between a model that performs and a model that protects. Medicine's oldest instruction has not moved. First, do no harm.
What you will learn:
- How to apply the Silence Audit to uncover silent data, including real-world social determinants and tacit clinical knowledge missing from datasets
- How to use the Validation Ladder to move safely from retrospective testing to Shadow Mode and Silent Trials, reducing the risk of deployment failures
- How to implement strategies such as Informative Missingness and Uncertainty Quantification so models can recognize data gaps and avoid unsafe predictions
- How to align interdisciplinary teams using the Translation Dictionary method to establish shared definitions of success before development begins
Who it is for:
This book is written for Technical Leaders and AI Architects in healthcare--including Senior Data Scientists, AI Product Managers, and Solution Architects--who are responsible for moving from model development to the safe, scalable deployment of clinical AI systems. It provides the frameworks they need to manage risk, safety, and real clinical value beyond accuracy metrics. It also serves Clinical Informatics professionals such as CMIOs, physician-builders, and health IT leaders who bridge clinical operations and engineering, offering a shared translation layer to convert clinical nuance into actionable technical requirements.
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