AI in Healthcare
Clinical Machine Learning, Medical Imaging, Safety and Governance
About this guide
A concise exam companion for AI in healthcare, written for medical and health students, not just computer scientists. It covers clinical machine learning from scratch, health data, medical imaging, clinical prediction and language, evaluation and validation in a clinical setting, bias and equity, safety and human factors, regulation, deployment, and genomics and drug discovery, closing on ethics and trust. Every chapter ends with a worked example carried through step by step, so you can check the reasoning yourself.
Inside the guide
- Machine learning from scratch
- Health data
- Medical imaging
- Clinical prediction and language
- Evaluation and validation in a clinical setting
- Bias and equity
- Safety and human factors
- Regulation
- Deployment
- Genomics and drug discovery
- Closing on ethics and trust
Questions students ask
What does AI in Healthcare cover?
It covers clinical machine learning from scratch, health data, medical imaging, clinical prediction and language, evaluation and validation in a clinical setting, bias and equity, safety and human factors, regulation, deployment, and genomics and drug discovery, closing on ethics and trust.
Who is AI in Healthcare for?
It is written for medical and health students meeting AI, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I read part of it for free?
Full Marks Press offers a free sample chapter at fullmarkspress.com/free, and the full guide is free to read with Kindle Unlimited.